CN107850048B - Method and system for generating a wind turbine control arrangement - Google Patents

Abstract

There is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising: determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data; applying an optimization function that changes an initial control schedule to determine an optimized control schedule by changing a tradeoff between fatigue life consumed by the turbine or one or more turbine components and energy capture until an optimized control schedule is determined, the optimization comprising: estimating a future fatigue life consumed by the turbine or turbine component during the altered control schedule based on the current remaining fatigue life and the altered control schedule; and constraining optimization of the control schedule in accordance with one or more input constraints; wherein the input constraints comprise a maximum allowable number of component replacements for one or more turbine components, and the optimizing further comprises changing an initial value of wind turbine life to determine a target wind turbine life.

Description

Method and system for generating wind turbine control arrangements

technical field

Embodiments of the present invention relate to methods and systems for determining a control schedule for wind turbine power output.

Background technique

FIG. 1A illustrates a large conventional wind turbine 1 known in the art, comprising a tower 10 and a wind turbine nacelle 20 located on top of the tower 10 . The wind turbine rotor 30 includes three wind turbine blades 32, each wind turbine blade having a length L. Wind turbine rotor 30 may include additional numbers of blades 32, such as one, two, four, five, or more. The blades 32 are mounted on a hub 34 at a height H above the base of the tower. The hub 34 is connected to the nacelle 20 by a low speed shaft (not shown) extending from the front of the nacelle 20 . The low speed shaft drives a gearbox (not shown) which increases the rotational speed and in turn drives a generator within the nacelle 20 for converting the energy extracted from the wind by the rotating blades 32 into electrical power output. The wind turbine blades 32 define a swept area A, which is the area of the circle traced by the rotating blades 32 . The swept area indicates how much of a given air mass is intercepted by the wind turbine 1 and, therefore, affects the power output of the wind turbine 1 and the forces and bending moments experienced by components of the turbine 1 during operation. Turbines can be onshore, as shown, or offshore. In the latter case, the tower will be attached to a monopile, tripod, trellis or other foundation structure, which may be fixed or floating.

For example, each wind turbine has a wind turbine controller, which may be located at the base of the tower or at the top of the tower. Wind turbine controllers process input from sensors and other control systems and generate output signals for actuators such as pitch actuators, generator torque controllers, generator contactors, switch, yaw motor, etc.

FIG. 1B schematically shows an example of a conventional wind power plant 100 including a plurality of wind turbines 110 , the controller of each wind turbine 110 in communication with a power plant controller (PPC) 130 . The PPC 130 may communicate bidirectionally with each turbine. The turbine outputs power to the grid connection point 140 as shown by the thick line 150 . In operation, and assuming wind conditions permit, each wind turbine 110 will output a maximum active power up to its rated power specified by the manufacturer.

Figure 2 illustrates a conventional power curve 55 for a wind turbine plotting wind speed on the x-axis versus power output on the y-axis. Curve 55 is the normal power curve of the wind turbine and defines the power output of the wind turbine generator as a function of wind speed. As is known in the art, the wind turbine starts generating electricity at the cut-in wind speed _Vmin . The turbine then operates under part load (also known as part load) conditions until the rated wind speed is reached at the _VR point. At rated wind speed, rated (or nominal) generator power is reached and the turbine is operating at full load. For example, the cut-in wind speed in a typical wind turbine may be 3 m/s and the rated wind speed may be 12 m/s. The V _max point is the cut-out wind speed, which is the highest wind speed below which the wind turbine can operate while delivering power. In situations where the wind speed is equal to or higher than the cut-out wind speed, the wind turbine is shut down for safety reasons (especially to reduce the loads acting on the wind turbine). Alternatively, the power output can be ramped down to zero power depending on the wind speed.

The power rating of a wind turbine is defined in IEC 61400 as the maximum continuous electrical power output the wind turbine is designed to achieve under normal operating and external conditions. Large commercial wind turbines are typically designed for a life of 20 to 25 years and are designed to operate at rated power so that the design loads and fatigue life of the components are not exceeded.

Fatigue damage accumulation rates for individual components in wind turbines vary widely under different operating conditions. The wear rate or damage accumulation tends to increase as the power generated increases. Wind conditions also affect the rate of damage accumulation. For some mechanical components, operating in very high turbulence can result in a rate of accumulation of fatigue damage that is many times higher than operating in normal turbulence. For some electrical components, operating at very high temperatures (which may be caused by high ambient temperatures) can result in a rate of accumulation of fatigue damage (eg, insulation breakdown rate) many times higher than operating at ambient temperature. For example, the rule of thumb for generator windings is that a 10°C drop in winding temperature increases life by 100%.

The annual power production (AEP) of a wind power plant relates to the production rate of the wind turbines that form the wind power plant, and is generally dependent on the annual wind speed at the location of the wind power plant. For a given wind farm, the larger the AEP, the greater the profit for the operator of the wind farm and the greater the amount of electrical energy supplied to the grid.

Consequently, wind turbine manufacturers and wind farm operators are continually trying to increase the AEP for a given wind farm.

One such approach may be to over-rate the wind turbine under certain conditions, in other words, allowing the wind turbine to operate to a power level above the wind turbine's rated or nameplate power level for a period of time, as shown in Figure 2. Shaded area 58 is shown in order to generate more electrical energy when the wind is strong and thus increase the AEP of the wind farm. In particular, the term "over-rating" is understood to mean the production of more than rated real power during full load operation by controlling turbine parameters such as rotor speed, torque or generator current. An increase in speed demand, torque demand and/or generator current demand increases the additional power generated by overrating, while a decrease in speed, torque and/or generator current demand reduces the additional power generated by overrating. Understandably, over-rating applies to active power, not reactive power. When the turbine is overrated, the turbine is running more aggressively than normal, and the generator has a power output that is higher than its rated power at a given wind speed. For example, the over-rated power level may be 30% higher than the rated power output. This allows for greater power extraction when this is beneficial to the operator, especially when external conditions such as wind speed, turbulence and electricity prices will allow for more profitable generation.

Over-rating results in higher wear or fatigue on components of the wind turbine, which can lead to early failure of one or more components and require shutdown of the turbine for maintenance. Therefore, over-rating is characterized by transient behavior. When the turbine is over-rated, it may be as short as a few seconds, or it may be an extended period of time if wind conditions and fatigue life of components favor over-rating.

While overrating allows turbine operators to increase AEP and otherwise modify generation to suit their requirements, there are several problems and disadvantages associated with overrating wind turbines. Wind turbines are typically designed to operate at a given nominal rated power level or nameplate power level and operate for a certified number of years (eg, 20 or 25 years). Therefore, if the wind turbine is over-rated, the life of the wind turbine may be reduced.

The present invention seeks to provide flexibility for turbine operators to operate their turbines in a way that suits their requirements, eg by returning an optimized AEP.

SUMMARY OF THE INVENTION

The invention is defined in the independent claims which are now referred to. Preferred features are listed in the dependent claims.

Embodiments of the present invention seek to increase the flexibility available to turbine operators when employing control methods that compromise energy capture and fatigue loading. An example of this control method is the use of overrating.

According to a first aspect of the present invention, there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the method comprising:

receiving input indicating a target minimum wind turbine lifetime;

determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data;

The parameters of the initial predefined control arrangement indicating how the maximum power level of the turbine changes over time are changed by:

i) adjusting the parameters of the initial predefined control arrangement;

ii) based on the changed control arrangement, estimating the future fatigue life consumed by the wind turbine or the one or more turbine components for the duration of the changed control arrangement; and

iii) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

Said parameters may be varied until the estimated future fatigue life consumed by the heaviest loaded components is sufficient to allow just reaching the target minimum wind turbine life, or in other words such that the total fatigue life consumed will be substantially the same as the target minimum wind turbine life same. This may be achieved based on a predetermined margin of target minimum wind turbine life (eg, within 0 to 1, 0 to 3, 0 to 6, or 0 to 12 months of the target).

Optionally, step (iii) further entails maximizing energy capture over the lifetime of the turbine.

Optionally, the control arrangement indicates that the wind turbine may be over-rated to an amount of power above its rated power.

Optionally, the method further comprises, for each of one or more of the turbine components, receiving an input indicative of a maximum allowable number of replacements for the turbine component. Step (i) may then further comprise, for one or more of the turbine components, adjusting the number of times that component may be replaced during the remaining life of the turbine. Step (i) may also include adjusting one or more of the turbine components when the components can be replaced during the remaining life of the turbine. The one or more turbine components may include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, yaw drives, Yaw bearing or transformer.

Optionally, the initial predefined control arrangement specifies a relative change in the maximum power level of the turbine over time.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises using data obtained from wind power plant sensors in conjunction with a site inspection procedure based on The data obtained by the plant sensors and parameters related to the wind plant and wind turbine design determine the loads acting on the turbine components. The sensor data may include sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, adjusting the parameters includes applying an offset, amplification, de-amplification or gain factor to the control arrangement. The parameters are adjusted until all or substantially all of the fatigue life of the most heavily loaded component is consumed within the scheduled duration. The offset can be adjusted by equalizing the area of the curve above and below the line showing the fatigue damage caused by operating the individual turbines at the maximum power level set to the site-specific capacity of the expected life. The offset can be adjusted until the fatigue damage over time due to operating the turbine according to the control schedule is equal to the fatigue damage over time due to operating the turbine according to a constant maximum power level, where the constant maximum power level is Individual turbine maximum power level set to target minimum life.

Optionally, an initially predefined control arrangement specifies a gradient of the maximum power level over time. Adjusting the parameters may then include adjusting the gradient.

Optionally, the control schedule indicates an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at a rate indicated by the control schedule.

Optionally, the method further comprises providing the determined control arrangement to the wind turbine controller to control the power output of the wind turbine.

This method can be executed only once, or from time to time as needed. Alternatively, the method can be repeated periodically. Specifically, the method can be repeated daily, monthly or yearly.

A corresponding controller for a wind turbine or wind power plant configured to perform the methods described herein may be provided.

Still according to a first aspect, there is provided a method for generating a control schedule for a wind power plant comprising two or more wind turbines, the control schedule indicating for each wind turbine how a maximum power level varies over time , the method includes:

receiving input indicating a target minimum expected life for each turbine;

determining a value indicative of the current remaining fatigue life of each wind turbine or one or more turbine components of each wind turbine based on the measured wind turbine site data and/or operational data;

Change the parameters of the initial predefined control arrangement specifying how the maximum power level of the power station changes over time by doing the following:

i) adjusting the parameters of the initial predefined control arrangement;

ii) Estimating the future fatigue life of the wind turbine or one or more turbine components consumed for the duration of the changed control arrangement, based on the changed control arrangement, using a site inspection procedure based on information obtained from wind power plant sensors data and parameters related to wind power plant and wind turbine design to determine the loads acting on turbine components and including interactions between wind power plant turbines; and

iii) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

Optionally, the sensor data includes sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, step (iii) is further constrained such that for any given time period within the arrangement, when the power of all turbines is added together, it does not exceed the amount that can be carried in the connection from the power station to the grid amount of power.

According to a second aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the method comprising:

receiving input indicating a maximum number of times each of the one or more turbine components will be replaced over the remaining life of the turbine;

determining a value indicative of the current remaining fatigue life of the turbine or one or more of the turbine components based on the measured wind turbine site data and/or operational data;

The parameters of the initial predefined control arrangement that dictate how the turbine's maximum power level changes over time are changed by:

iv) adjusting the parameters of the initial predefined control arrangement;

v) estimating the future fatigue life that the wind turbine or one or more turbine components will consume over the duration of the changed control arrangement based on the changed control arrangement and taking into account the replacement of one or more turbine components; and

vi) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

This parameter can be varied until the estimated future fatigue life consumed by the heaviest loaded components is sufficient to allow just reaching the target minimum wind turbine life, or in other words such that the total fatigue life consumed will be substantially equal to the target minimum wind turbine life . This may be achieved based on a predetermined margin of target minimum wind turbine life (eg, within 0 to 1, 0 to 3, 0 to 6, or 0 to 12 months of the target).

Optionally, step (iii) further entails maximizing energy capture over the lifetime of the turbine.

Optionally, the control arrangement indicates that the wind turbine may be over-rated to an amount of power above its rated power.

Optionally, step (i) further comprises adjusting, for one or more turbine components, the number of times the components may be replaced during the remaining life of the turbine. Step (i) may further include adjusting for one or more turbine components when components may be replaced during the remaining life of the turbine.

Optionally, the target minimum wind turbine life is a predetermined target value corresponding to the turbine design life.

Optionally, the method further includes receiving input indicative of a user-defined target minimum wind turbine life.

Optionally, the initial predefined control schedule specifies a relative change in turbine maximum power level over time.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components comprises: using data obtained from the wind power plant sensors in conjunction with a site inspection procedure, the site inspection procedure being based on the data obtained from the wind power plant sensors; Data and parameters related to wind power plant and wind turbine design to determine the loads acting on turbine components. The sensor data may include sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, adjusting the parameters includes applying an offset, amplification, de-amplification or gain factor to the control arrangement. The parameters are adjusted until all or substantially all of the fatigue life of the most heavily loaded component is consumed within the scheduled duration. The offset can be adjusted by equalizing the area of the curve above and below the line showing the fatigue damage caused by operating the individual turbines at the maximum power level set to the site-specific capacity of the expected life. The offset can be adjusted until the fatigue damage over time due to operating the turbine according to the control schedule is equal to the fatigue damage over time due to operating the turbine according to a constant maximum power level, where the constant maximum power level is Individual turbine maximum power level set to target minimum life.

Optionally, the initial predefined control schedule specifies a gradient of change in the maximum power level over time. Adjusting the parameters may include adjusting the gradient.

Optionally, the control arrangement indicates an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at a rate indicated by the control arrangement.

Optionally, the method further comprises providing the determined control arrangement to the wind turbine controller to control the power output of the wind turbine.

Optionally, the one or more turbine components include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, offsets Air drives, yaw bearings or transformers.

This method can be executed only once, or from time to time as needed. Alternatively, the method can be repeated periodically. Specifically, the method can be repeated daily, monthly or yearly.

A corresponding controller for a wind turbine or wind power plant configured to perform the methods described herein may be provided.

Still according to a second aspect, there is provided a method for generating a control schedule for a wind power plant comprising two or more wind turbines, the control schedule indicating for each wind turbine how a maximum power level varies over time , the method includes:

receiving input indicating a maximum number of times each of the one or more turbine components of each turbine is to be replaced during the remaining life of the turbine;

determining a value indicative of the current remaining fatigue life of each wind turbine or one or more turbine components of each wind turbine based on the measured wind turbine site data and/or operational data;

Change the parameters of the initial predefined control arrangement specifying how the maximum power level of the power station changes over time by doing the following:

iv) adjusting the parameters of the initial predefined control arrangement;

v) using a site inspection procedure to estimate the future fatigue life of the wind turbine or one or more turbine components consumed for the duration of the changed control arrangement based on the changed control arrangement and considering replacement of one or more turbine components, the site The inspection procedure determines the loads acting on the turbine components, and includes the interaction between the turbines of the wind power plant, based on data obtained from the wind power plant sensors and parameters related to the wind power plant and wind turbine design; and

vi) Repeat steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

Optionally, the sensor data includes sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, step (iii) is further constrained such that for any given time period within the arrangement, when the power of all turbines is added together, it does not exceed the amount that can be carried in the connection from the power station to the grid amount of power.

According to a third aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data;

Applying an optimization function that alters an initial control arrangement to determine an optimization by altering the trade-off between fatigue life consumed by the turbine or the one or more turbine components and energy capture until an optimized control arrangement is determined The control arrangement, the optimization includes:

estimating future fatigue life consumed by the turbine or turbine component during the changed control schedule based on the current remaining fatigue life and the changed control schedule; and

constrain the optimization of the control arrangement according to one or more input constraints;

Wherein the input constraints include a maximum allowable number of component replacements for one or more turbine components, and the optimizing further includes changing an initial value of wind turbine life to determine a target wind turbine life.

According to a fourth aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data;

Applying an optimization function that alters an initial control arrangement to determine an optimization by altering the trade-off between fatigue life consumed by the turbine or the one or more turbine components and energy capture until an optimized control arrangement is determined The control arrangement, the optimization includes:

estimating future fatigue life consumed by the turbine or turbine component during the changed control schedule based on the current remaining fatigue life and the changed control schedule; and

constrain the optimization of the control arrangement according to one or more input constraints;

Wherein the input constraints include a target minimum wind turbine life, and the optimizing further includes changing an initial value for the number of component replacements to be performed during the scheduled period for one or more components to determine a maximum number of component replacements.

According to a fifth aspect of the present invention, there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data;

Applying an optimization function that alters an initial control arrangement to determine an optimization by altering the trade-off between fatigue life consumed by the turbine or the one or more turbine components and energy capture until an optimized control arrangement is determined The control arrangement, the optimization includes:

estimating future fatigue life consumed by the turbine or turbine component during the changed control schedule based on the current remaining fatigue life and the changed control schedule; and

constrain the optimization of the control arrangement according to one or more input constraints;

wherein the optimizing further includes changing an initial value of wind turbine life and changing an initial value for the number of component replacements to be performed during the scheduled period for one or more components to determine a target minimum wind turbine life and one or more A combination of component replacements for each turbine component.

The following optional features may apply to the third, fourth or fifth aspect.

The control arrangement can be applied throughout the life of the turbine.

Optionally, the method further includes optimizing the control schedule by varying the timing of component replacements and varying the number of component replacements until a maximum number is reached.

Optionally, the replaceable one or more turbine components include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, Yaw drive, yaw bearing or transformer.

Optionally, the initial control arrangement specifies a relative change over time of the maximum turbine power level at which the turbine may operate.

Optionally, the input constraints also include an upper limit maximum power output of the turbine and/or a minimum power output of the turbine allowed by the turbine design.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data obtained from the wind farm sensors in conjunction with a site inspection procedure based on the wind farm sensors and in conjunction with the wind farm and the wind farm. Turbine design-related parameters to determine the loads acting on turbine components.

Optionally, the optimization of the control arrangement includes changing the control arrangement to minimize the levelized cost of energy (LCoE). LCoE can be determined using an LCoE model that includes parameters for one or more of the following: A capacity factor, which indicates the amount of energy generated during a time period divided by the amount the turbine can generate if the turbine were to operate continuously at rated power for that time period availability, which indicates how long the turbines can be used to generate electricity; and wind farm efficiency, which indicates the energy generated during the time period divided by the energy the turbines could generate if they were operating in wind completely undisturbed by upstream turbines. The model may further include parameters for one or more of the following: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and refurbished Transportation costs of parts or said replacement parts to the power station; and service costs associated with replacing worn components.

Optionally, the optimized control arrangement is an arrangement of a maximum power level at which the turbine can operate, and a maximum power level higher than the rated power of the wind turbine may be specified. Optionally, the control schedule may specify an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at a rate indicated by the control schedule.

The control arrangement may dictate how the turbine maximum power level changes over the life of the turbine.

Optionally, the method may further comprise providing the optimized control arrangement to the wind turbine controller or the wind power plant controller to control the power output of the wind turbine.

Optionally, the method is repeated periodically. This method can be repeated daily, monthly or yearly.

A corresponding controller for a wind turbine or wind power plant configured to perform the method of the third, fourth or fifth aspect described herein may be provided.

According to a third aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operating variables of the wind turbine and include initial control arrangements; one or more constraints; and an indication of the turbine or a or data on the current remaining fatigue life of multiple turbine components;

Wherein, the optimization module is configured as:

The optimization is performed by changing one or more of the variables from their initial values based on the remaining fatigue life of the turbine or the one or more turbine components and the one or more constraints. maximizing or minimizing operating parameters received at the module depending on the set of variables to optimize the control arrangement; and

outputting said optimized control arrangement;

Wherein the constraints include a maximum allowable number of component replacements for one or more turbine components, and the optimization module is further configured to vary the initial value of wind turbine life to determine a target wind turbine life.

According to a fourth aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operating variables of the wind turbine and include initial control arrangements; one or more constraints; and an indication of the turbine or a or data on the current remaining fatigue life of multiple turbine components;

Wherein, the optimization module is configured as:

The optimization is performed by changing one or more of the variables from their initial values based on the remaining fatigue life of the turbine or the one or more turbine components and the one or more constraints. maximizing or minimizing operating parameters received at the module depending on the set of variables to optimize the control arrangement; and

outputting said optimized control arrangement;

wherein the constraints include a target minimum wind turbine life, and the optimization module is further configured to vary an initial value of a number of component replacements to be performed during the scheduled period for one or more components to determine a maximum number of component replacements .

According to a fifth aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operating variables of the wind turbine and include initial control arrangements; one or more constraints; and an indication of the turbine or a or data on the current remaining fatigue life of multiple turbine components;

Wherein, the optimization module is configured as:

The optimization is performed by changing one or more of the variables from their initial values based on the remaining fatigue life of the turbine or the one or more turbine components and the one or more constraints. maximizing or minimizing operating parameters received at the module depending on the set of variables to optimize the control arrangement; and

outputting said optimized control arrangement;

wherein the optimization module is further configured to change the initial value of the wind turbine life and change the initial value of the number of component replacements to be performed during the scheduled period for one or more components to determine the target minimum wind turbine life and A combination of component replacement times for one or more turbine components.

The following optional features may apply to the optimizer of the third, fourth or fifth aspect.

Optionally, the initial control arrangement specifies a relative change over time of the maximum turbine power level at which the turbine may operate.

Optionally, the optimizer further includes an initialization module configured to receive the sensor data and initial values of the variable set, the initialization module configured to calculate the initial values of the operating parameters.

Optionally, the one or more turbine components are one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, offsets Air drives, yaw bearings or transformers.

Optionally, the operating parameter is the levelized cost of energy (LCoE) of the turbine, and optimizing the control arrangement includes minimizing the levelized cost of energy (LCoE). LCoE can be determined using an LCoE model that includes parameters for one or more of the following: A capacity factor, which indicates the amount of energy generated during a time period divided by the amount the turbine can generate if the turbine were to operate continuously at rated power for that time period availability, which indicates how long the turbines can be used to generate electricity; and wind farm efficiency, which indicates the energy generated during the time period divided by the energy the turbines could generate if they were operating in wind completely undisturbed by upstream turbines. The model may further include parameters for one or more of the following: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and refurbished Transportation costs of parts or said replacement parts to the power station; and service costs associated with replacing worn components.

A controller comprising an optimizer according to any of the third, fourth or fifth aspects may be provided.

According to a third aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating for each wind turbine how the maximum power level of the turbine varies over time, the method include:

determining a value indicative of the current remaining fatigue life of each turbine or one or more turbine components of each turbine based on the measured wind turbine site data and/or operational data;

Apply an optimization function that alters the performance of each turbine by changing the trade-off between fatigue life consumed and energy capture per turbine or one or more turbine components of each turbine until an optimal control arrangement is determined. The initial control schedule determines an optimized control schedule including:

The future fatigue life consumed by the turbine or turbine components during the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design to determine loads acting on turbine components and including interactions between the turbines of the wind power plant; and

Constraining optimization of the control arrangement according to one or more input constraints;

wherein the constraints include a maximum allowable number of component replacements for each of the one or more turbine components of each of the wind turbines, and the optimization module is further configured to vary wind turbine life to determine the target wind turbine lifetime.

According to a fourth aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating, for each wind turbine, how the turbine maximum power level varies over time, the method include:

determining a value indicative of the current remaining fatigue life of each turbine or one or more turbine components of each turbine based on the measured wind turbine site data and/or operational data;

Apply an optimization function that alters the performance of each turbine by changing the trade-off between fatigue life consumed and energy capture per turbine or one or more turbine components of each turbine until an optimal control arrangement is determined. The initial control schedule determines an optimized control schedule including:

The future fatigue life consumed by the turbine or turbine components during the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design to determine loads acting on turbine components and including interactions between the turbines of the wind power plant; and

Constraining optimization of the control arrangement according to one or more input constraints;

wherein the constraints include a target minimum wind turbine life for each of the wind turbines, and the optimization module is further configured to change, for each wind turbine, one or more component changes to be during a scheduled period The initial value of the number of parts replacements performed to determine the maximum number of parts replacements.

According to a fifth aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating, for each wind turbine, how the turbine maximum power level varies over time, the method include:

determining a value indicative of the current remaining fatigue life of each turbine or one or more turbine components of each turbine based on the measured wind turbine site data and/or operational data;

Apply an optimization function that alters the performance of each turbine by changing the trade-off between fatigue life consumed and energy capture per turbine or one or more turbine components of each turbine until an optimal control arrangement is determined. The initial control schedule determines an optimized control schedule including:

The future fatigue life consumed by the turbine or turbine components during the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design to determine loads acting on turbine components and including interactions between the turbines of the wind power plant; and

Constraining optimization of the control arrangement according to one or more input constraints;

wherein the optimizing further includes changing an initial value for the lifetime of each wind turbine, and changing an initial value for the number of component replacements to be performed during the scheduled period for one or more components of each wind turbine to determine each A combination of a target minimum wind turbine life for the wind turbines and the number of component replacements for one or more turbine components of each wind turbine.

The following optional features may apply to the plant level method of the third, fourth or fifth aspect.

Optionally, the initial control schedule specifies, for each turbine, the relative change over time of the maximum turbine power level at which the turbine may operate.

Optionally, the sensor data includes sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

For one or more turbine components, optionally, the optimization function changes the number of times the components can be replaced over the remaining life of the turbine. The optimization function makes changes to when one or more of the turbine components can be replaced during the remaining life of the turbine.

Optionally, the method is further constrained such that for any given period of time within the arrangement, when the power of all turbines is added together, it does not exceed the amount of power that can be carried in the connection from the power station to the grid .

A corresponding wind power plant controller configured to perform the above method of the third, fourth or fifth aspect may be provided.

Any of the methods described herein may be embodied in software that, when executed on a processor of a controller, causes it to perform the associated method.

References herein to site inspection software include site inspection tools known to those skilled in the art for simulating the operation of wind turbines to determine wind power based on pre-construction and/or pre-commissioning sensor data and other site information (eg, terrain, etc.) Operating characteristics of turbines and wind power plants. The site inspection tool may also use operational data from turbines or power plants, or may use operational data from similar turbines or power plants, where such data is available. Examples include the Vestas(TM) site inspection tool. DNV GL provides an alternative site checking package. It consists of three linked programs: "WindFarmer", "WindFarmer Bladed Link" and "Bladed", which allow the user to perform comprehensive performance and load calculations.

Description of drawings

The invention will now be further described, by way of example only, with reference to the accompanying drawings, in which:

1A is a schematic front view of a conventional wind turbine;

FIG. 1B is a schematic diagram of a conventional wind power plant including a plurality of wind turbines;

2 is a graph illustrating a conventional power curve for a wind turbine;

3 is a graph illustrating how the power produced by a wind turbine over time varies with the target life of the turbine;

4 is a diagram illustrating different power arrangements for wind turbines, wherein individual maximum wind turbine power levels are varied over the life of the turbine to control power output;

FIG. 5 is a graph showing exemplary variation in accumulated total life fatigue between different turbine components;

6 is an example of a simplified levelized energy cost model for a wind power plant;

7 is a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy;

FIG. 8 is an example of a method for determining a wind turbine type maximum power level; and

Figure 9 is a schematic diagram of a wind turbine controller arrangement.

Detailed ways

Embodiments of the present invention seek to increase the flexibility a turbine operator may have when employing a control method that compromises energy capture and fatigue loading. Specifically, embodiments provide an optimization method to allow turbine operators to optimize turbine performance, such as AEP, according to their requirements.

To optimize performance, three parameters can be varied throughout the wind turbine control strategy. These are: (i) the power schedule of the wind turbine; (ii) the remaining life of the wind turbine; (iii) the number of component replacements allowed during the remaining life of the wind turbine. One or more of these parameters may be varied relative to one or more of the other parameters to achieve an optimized control strategy. Parameters may also be restricted by constraints.

For example, optimizations can be performed to increase the AEP of the turbine over its entire life and increase profitability. The turbine operator can specify one or more constraints and can then perform optimization. The operator may request one or more of a minimum wind turbine life (eg, 19 years), a maximum number of replacements of individual components (eg, one gearbox replacement), and/or a specific power schedule, schedule curve or shape, or schedule gradient.

A power schedule is an arrangement of variables used by the wind turbine controller to trade off energy capture and fatigue loads during the remainder of the turbine's life, such as when the turbine is overrated. The additional power generated by overrating a given turbine can be controlled by specifying values for variables such as individual wind turbine maximum power levels. The maximum power level specifies power above rated power, up to the power at which the turbine can operate while overrated. A power schedule may specify a constant maximum power level over the life of the turbine. Alternatively, the power schedule may specify a maximum power level that varies over the life of the wind turbine so that the amount of additional power generated by overrating may vary over time. For example, a power station operator may wish to generate more electricity during the early years of a wind turbine's life at the expense of increased fatigue life consumption of turbine components, since the financial value of electricity generation during the early years of the project is disproportionately high.

For a given turbine type, the individual wind turbine maximum power levels are constrained by the maximum load limits of the mechanical components of the wind turbine as well as the design limits of the electrical components, as the maximum power cannot be safely increased beyond subjecting the turbine to above its maximum design load limit the level of electrical load or mechanical load value. The upper maximum power level that the individual wind turbine maximum power level cannot exceed may be referred to as the "wind turbine type maximum power level" and specifies the maximum power level at which the determined load does not exceed this maximum power level. Design loads for types of wind turbines. An example of how the wind turbine type maximum power level can be calculated is given in the "Maximum Power Level Calculation" section below.

The individual wind turbine maximum power level is the power level specified in the arrangement according to embodiments of the present invention, and may simply be referred to as the maximum power level. Individual wind turbine maximum power levels may be refined for each individual turbine, based on one or more of the conditions each turbine faces at a particular location or location in the wind power plant, based on each turbine's fatigue loads value, where an individual wind turbine maximum power level is determined for each turbine at a given site. The individual wind turbine maximum power levels can then be set such that the rate of consumption of the fatigue life of the turbine or individual turbine components gives a fatigue life that corresponds to or exceeds a particular target life.

The remaining life of the wind turbine specifies the amount of operating life that the operator is willing to accept in order to optimize the AEP. The remaining life will depend on the point in time when the first activation of the AEP optimization method is implemented, as the available remaining life decreases as the turbine operates.

The number of component replacements allowed during the remaining life of the wind turbine can also be used to optimize the AEP. Due to different rates of turbine component fatigue under different conditions, the actual life of some components may well exceed the expected 20-year life of the wind turbine, or similarly, these components can be overrated by a higher amount during a given life cycle run. Components with a longer life cannot drive the entire turbine life and have excess production capacity. However, components with shorter lifespans may have a limiting effect on overrating, and the AEP can be increased by replacing one or more of these components during the life of the turbine. In particular, over-rating achieved by increasing torque has a particularly significant impact on the fatigue life of gearboxes, generators and power output components. Conversely, where over-rating is achieved by increasing rotor speed, then the fatigue life of blades and structural components is more affected.

Replaceable components in the context of embodiments of the present invention are considered major components, such as those components that each account for 5% or more of the total cost of the wind turbine and can be replaced in the field. General wear parts that make up only a fraction of the total cost of a wind turbine do not need to be considered. In particular, components under consideration for replacement may include one or more of blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, yaw drives, yaw bearings or transformers multiple.

Figure 3 shows a first example of optimization where the power schedule is varied relative to the target life of the turbine. In this example, the design life of the turbine is 20 years, and the power level is fixed during the life of the turbine. It can be seen that the amount of electricity produced in a given year increases as the life of the wind turbine decreases. As turbine life decreases, the fatigue life consumption rate of the turbine or turbine components may increase, allowing additional power to be generated by overrating. Optimization can be applied according to the turbine operator's preference. For example, a lifetime that maximizes the turbine's AEP, net present value (NPV), or pure present value (NPW) may be determined and selected. NPV/NPW can be calculated using known methods.

Figure 4 shows another example of optimization, where the power schedule is again varied relative to the target life of the turbine. In this example, the maximum power level specified by the schedule is variable over the life of the turbine. The initial arrangement may be specified, eg, the turbine operator may have the desired arrangement shape to be used. Scheduling defines how individual wind turbine maximum power levels vary over time, but can be done in a relative rather than absolute manner. In this example, the desired schedule 401 is a linear schedule from a wind turbine type maximum power level P _max to a turbine type nominal or rated power level P _nom over a 20 year turbine life. For a typical example site where the annual average wind speed is below the turbine design wind speed, the dotted line A represents the site-specific capacity of the individual turbines over a 20-year life. For a particular turbine, it may not be possible to meet the desired arrangement 401 without exceeding the fatigue life of the turbine or certain turbine components during the life of the turbine. The schedule is therefore adjusted until the total fatigue that occurs according to the power schedule does not exceed the design fatigue life of the most heavily loaded component.

This can be achieved by estimating the fatigue damage that will occur following the schedule for its duration (eg, up to the turbine design life, or user specified turbine life). Fatigue damage that occurs can be estimated using the site check function, and LUE data can be supplemented, both taking into account the fatigue damage due to loading given the conditions of the microsite. The arrangement can be adjusted until the resulting fatigue life of the most heavily loaded component equals the design fatigue life of that component. In other words, the schedule is adjusted until all or most of the fatigue life of the most heavily loaded component is used up during the scheduled duration.

An arrangement can be adjusted by adjusting one or more of its parameters. This can include:

- apply an offset to an arrangement by adding or subtracting a value to the entire arrangement;

- apply a gain greater or less than 1 to the arrangement;

- Non-linearly raising or lowering any other suitable function of the control arrangement by adjusting the relevant parameters to otherwise expand/contract or increase/decrease the arrangement as needed to change the arrangement power level value.

In one example, the adjustment to the schedule may be accomplished by: based on an equivalence plot of occurred fatigue damage versus time or an equivalence plot of residual fatigue life versus time for the most fatigued components determined from a power schedule , and use site inspection software to determine fatigue damage to components that may occur at specific turbine locations (also referred to as turbine microsites) within the power plant at a given power level. The curves are adjusted until the area defined by each arrangement above and below the corresponding capability line on the equivalent fatigue curve for the desired turbine life is equal. This can be achieved, for example, by equalizing the areas of the curves above and below the line showing fatigue damage caused by individual turbines operating at a constant maximum power level set to the site-specific capability over the expected life . For example, this would be a line equivalent to dashed line A of Figure 3, but showing fatigue damage over time due to the maximum power of an individual wind turbine. Area equivalence can be achieved by adding or subtracting an offset to the curve to move the power scheduling curve up or down until the areas are equal, or by scaling one or more parameters of the curve to enlarge or shrink the curve. The total fatigue life consumed by the turbine or turbine components will be 20 years of operation. An exemplary arrangement is shown by line 402, which terminates in black square i.

The site-specific capabilities of the turbines over a 19-year lifetime are shown in dashed line B for the same exemplary site. It can be seen that the capacity during the 19-year lifespan exceeds the capacity during the 20-year lifespan. Thus, the resulting 19-year arrangement, exemplified by line 403 , may have a higher initial maximum power level value P′ _19yrs than the initial maximum power level value P′ _20yrs of the 20-year life arrangement 402 . Arrangement 403 ends in year 19, represented by the black square ii.

In the example of FIG. 4, the schedule adjustment is subject to the additional constraint that the slope or gradient of the schedule should be equal to the slope or gradient of the initial schedule 401 for the 20-year life. As used in the example of Figure 4, other constraints may also be applied whereby the slope of the arrangement is equal to the slope of the initial arrangement 401 until a nominal power level is reached, which may be the rated power of the turbine , from which point the maximum power level is maintained at the nominal power level. Alternatively, embodiments may employ derating of the turbine such that the maximum power level specified by the arrangement may be set to a level lower than the rated power of the turbine.

The schedule is adjusted in a stepwise manner, either decreasing from _Pmax , or increasing from _Pnom , or increasing from the power value of line A, until an appropriate schedule is reached for which, in the most heavily loaded turbine component Have sufficient fatigue life to achieve target turbine life. For example, the initial maximum power level P' may be increased or decreased in steps of 1% of _Pnom until an appropriate schedule is reached.

There are other possibilities to optimize the power schedule depending on the age of the turbine's life. For example, the schedules may all start from the same initial value (eg, _Pmax ) and ramped until the area defined by each schedule above and below the corresponding capability line on the equivalent fatigue curve suitable for the expected turbine life is equal until.

Another line 404 shows an example of an arrangement that a turbine can achieve over a 20-year lifetime if one or more component replacements are considered. Arrangement 404 ends with black box i. One or more components may be particularly susceptible to fatigue damage from over-rating. For example, as shown in Figure 5, after 20 years of operation, one component may reach the 20-year life fatigue limit, while other components still have a certain amount of life remaining. In this case, replacing one or more components causing the higher fatigue damage rate allows the AEP to increase. This may still increase the profitability of the turbine when calculating NPV over the lifetime of the turbine, including and taking into account the total cost of replacement.

As an alternative to specifying a schedule for maximum power level values, schedules for fatigue damage or remaining fatigue life can also be specified, since the rate of induced fatigue damage is related to the maximum power level setting of the turbine. The turbine power output is then controlled such that the remaining fatigue life remains at the remaining fatigue life specified by the schedule, eg, by using the LUE to track the fatigue life of the turbine controller. As another alternative, energy scheduling can also be used, as this still dictates how the turbine maximum power level varies over time. The energy schedule can be yearly or every calendar month etc.

For the avoidance of doubt, the arrangement may also have a non-linear shape, such as a shape following a polynomial curve.

While the schedules are shown to vary continuously over their duration, they can vary in steps, specifying a given maximum power level during a specific time period (eg, a month, a season, or a year). The schedule may be, for example, a series of annual values over the life of the turbine.

A schedule can be calculated once, or it can be repeated at intervals. For example, the schedule can be monthly or yearly. For a schedule that specifies a maximum power level annually, it may be advantageous to, for example, calculate the schedule every month or week, as changes to the schedule may alert the user to parameters that are changing faster than expected.

If the calculation is scheduled once, the calculation can be done before the commissioning of the wind power plant, or it can be done at any time after the commissioning. For calculations that are repeated at regular intervals, the first calculation can be done before the commissioning of the wind power plant, or it can be done at any time after the commissioning.

- first example

According to a first example, a control arrangement is generated that can be used to control a wind turbine. Relative arrangements may be defined, and one or more of a minimum wind turbine life or a maximum number of replacements of major components may be defined. The arrangement is then adjusted to ensure that the fatigue life of the turbine reaches the target life while maximizing the AEP.

The wind turbine operates according to one of the overrating control techniques described herein using an overrating controller, which may be implemented by the wind turbine controller.

A lifetime usage estimator (LUE) can be used to determine and monitor the lifetime usage of components. A life usage estimator can be used to ensure that the fatigue load limits of all turbine components remain within their design lives. The loads experienced by given components (eg, their bending moments, temperatures, forces, or motion) can be measured and the amount of component fatigue life consumed can be calculated, for example, using techniques such as rainflow counting and Miner's law or chemical decay equations . Based on the lifetime usage estimator, individual turbines can then operate in a manner that does not exceed their design limits. Equipment, modules, software components or logic components used to measure the fatigue life consumed by a given turbine component may also be referred to as its life usage estimator, and will use the same acronym (LUE) to refer to for determining The lifetime is estimated using algorithms and corresponding equipment, modules or software or logic components. The LUE will be described in more detail below.

According to the default operating mode, the over-rating controller will control the amount of over-rating applied based on the function or schedule during the expected or certified lifetime of the wind turbine. Usually this is 20 or 25 years.

The controller is configured to receive input parameters, eg, from a site operator, the input defining a new target life for the wind turbine or one or more specific turbine components. Use the LUE to determine the lifetime usage of the turbine or associated turbine components to date. This has constraints on the amount of component life remaining for the wind turbine, and therefore on the control arrangements. Additionally, the revised target life has a constraint on the amount of time the remaining component life must be extended.

Future available fatigue life can be calculated off-line or on-line using site inspection software and used to specify revised control arrangements. Site inspection functions may include calculations or one or more simulations to use historical site-based data (including measured site climate data prior to construction and/or post-construction measured site climate data and/or data from the LUE) to Determine the expected fatigue damage rate. Site climate data typically includes data from wind masts or base-station lidars, and can include wind speed, turbulence intensity, wind direction, air density, vertical wind shear, and temperature. Site inspection calculations may be performed remotely or by the turbine/generator level controller as appropriate.

The site inspection software may have information or parameters related to a given WPP site topography, topography, wind conditions, etc. Topographic and topographic information may be provided through site surveys and/or knowledge from the WPP site, which may include slopes, cliffs, inflow angles for each turbine in the WPP, and the like. Wind conditions, such as wind speed (seasonal, annual, etc.), turbulence intensity (seasonal, annual, etc.), air density (seasonal, annual, etc.), temperature (seasonal, annual, etc.), can be obtained from wind mast data and/or wind turbines and/or wind conditions experienced and recorded by the WPPC at the WPP.

The site inspection tool may include one or more memories, databases, or other data structures to store and maintain fatigue load values for each type of wind turbine, wind turbine type maximum power levels for each type of wind turbine, and correlation with WPP site conditions related information and/or parameters.

A revised control arrangement is therefore generated whereby the additional power generated by the over-rating is adjusted such that the turbine is exposed to a higher or lower rate of accumulation of fatigue damage, depending on whether the new target end-of-life date is earlier or earlier than the previous target date Later, the previous target date can be the lifetime of the certification.

The ability to modify turbine control arrangements allows operators to change their priorities over time. For example, the main generator on the local grid may be taken out of service for mid-term maintenance, or it may be taken out of service entirely and the grid may need additional support. This may be reflected in much higher long-term costs, so it is beneficial for operators to increase energy production in the short term. Therefore, operators can decide to reduce the life of the turbine, or reduce the life of affected components such as gearboxes and generators, and generate additional power by over-rating, while accepting a shorter wind turbine or turbine component life.

Methods other than the LUE may be used to determine the lifetime usage of the wind turbine or turbine components. Alternatively, the operational dates of the turbine can be checked and the fatigue damage that has occurred to date can be calculated. This can be particularly useful when over-rating control is updated to wind turbines, and the future available fatigue life is calculated off-line again using site checking software, and this is used to specify maximum power levels. The site inspection function can again include off-line or on-line calculations, or one or more simulations to determine expected fatigue damage rates using measured site data up to the time of installation or based on historical site data, although in this case the calculations are not No LUE data is required to be available.

Site check software may be used to check the operation of wind turbines up to the date of installation of an over-rated controller using the functions described herein, to use with the wind power plant site based on input parameters specifying site topography, site topography, site weather conditions, etc. To calculate fatigue loads on turbine components, such as energy output, wind speed, wind direction, turbulence intensity, wind shear, air density, turbomachinery load measurements (e.g. , from blade load sensors) one or more of turbine electrical component temperatures and loads, icing events, component temperatures, and condition monitoring system outputs. These values can be used to calculate an estimate of the fatigue damage that has occurred on turbine components to date. The future usable life of a turbine or turbine component can be calculated by applying the measurements to a site inspection function wind turbine model or simulation based on the value of the wind turbine type maximum power level of the turbine and among these measurements one or more of to provide estimated fatigue damage and/or residual fatigue life as output. The simulation or model may provide fatigue damage and/or residual fatigue life at the component level or for the turbine as a whole. Fatigue load calculations can be performed according to various calculation programs. Various examples of these site inspection procedures will be known to those skilled in the art and will not be described in detail.

The resulting estimates of exhausted fatigue life of the turbine or turbine components may be used to determine an overrating strategy applied by the controller. This estimate may be used once at the initialization of the over-rated control, and if updated, may be performed halfway through the life of the turbine. Alternatively, the estimation may be performed periodically during the life of the turbine, such that the over-rating strategy is periodically updated according to how the life fatigue consumption changes throughout the life of the turbine.

The over-rating strategy is determined based on the remaining fatigue life of the wind turbine or wind turbine components, which is itself based on the operating life of the wind turbine. The amount of over-rating applied is controlled such that the turbine or turbine components cause fatigue damage at a rate low enough to ensure that the fatigue life of the turbine is used only at the end, and preferably only at the end of the intended turbine life Finish.

The determination of component fatigue life estimates may be further extended or substituted by using data from one or more condition monitoring systems. Condition Monitoring Systems (CMS) include a number of sensors at strategic points in the driveline, in turbine gearboxes, generators or other critical components. Condition monitoring systems provide early warning of component failure before the component actually fails. Accordingly, the output from the condition monitoring system may be provided to the controller and used as an indication of the fatigue life consumed by the monitored component, and may in particular provide an indication of when the fatigue life of the component has reached its end. This provides an additional method of estimating the useful life.

- second example

A second example is provided to perform a more general optimization process that can be used to perform optimizations similar to those described above, as well as other more general optimizations. The optimization process of the second example may be performed by the controller applying the optimization scheme.

The full financial cost or levelized cost of energy (LCoE) model of the turbine is included and can be used in offline calculations prior to installation of the over-rated control system, or online as part of a wind turbine controller or wind farm controller. The use of the LCoE model allows for optimization of over-rating strategies and can also factor in the replacement of major components based on the cost of doing so. As used herein, the term "levelized energy cost" refers to a measure of energy cost from a turbine calculated by dividing the turbine's lifetime cost by the turbine's lifetime energy output.

Figure 6 shows an example of a simplified LCoE model where various costs associated with building and operating wind turbines and wind turbine power plants are considered.

Wind turbine generator (WTG) costs take into account the overall cost of manufacturing the wind turbine. Transportation costs Factor in the cost of transporting turbine components to the site for installation. Operation and maintenance (O&M) costs factor in turbine operating costs and can be updated as O&M occurs. This information can be provided by the service technician to the local turbine controller, to the wind farm controller, or otherwise. The capacity factor represents the energy generated during a given time period (eg, one year) divided by the energy that would have been generated during that time period if the turbine were running continuously at rated power. Availability indicates how long a turbine is available to generate electricity. Plant efficiency represents the efficiency with which energy is extracted from the wind and is affected by the spacing of the turbines within the plant.

Only those elements of the LCoE that are affected by control and component replacement strategies need to be included in the LCoE model because many parameters that can be included in the LCoE model are fixed when building a turbine or wind farm. The affected elements are:

· Operation and maintenance (O&M) costs

如果更换更多部件，则会增加

Increases if more parts are replaced

·Capacity factor

如果使用更积极的超额定，则会增加，因此，会生成更多的MWh

If a more aggressive overrating is used, it will increase and, therefore, generate more MWh

● Availability

如果更换更多的主要部件，则由于更换过程所需的停机时间，会稍微减少

If more major components are replaced, there is a slight reduction due to the downtime required for the replacement process

如果更积极的超额定导致磨损部件的增多的预防性更换或计划外故障，则会稍微减少

Slightly reduced if more aggressive over-rating results in increased preventative replacement of worn parts or unplanned failures

●Life

取决于约束选择而减少或增加。

Decrease or increase depending on constraint selection.

A financial cost (LCoE) model with turbines included in the turbine or WPP controller allows for the determination of more flexible and efficient control strategies. For example, if conditions at a particular location are found to be particularly harsh on the gearbox, such conditions will be identified and the operator may choose whether to over-rate the turbine and take into account a certain number of gearbox replacements. The turbine controller can then determine when the gearbox should be replaced, operate the turbine accordingly, and optionally also provide an indication of when to replace the gearbox.

Figure 7 shows a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy that may be incorporated into a controller and that may be used to implement various embodiments of the present invention.

Blocks marked as "initialized" will run once when the algorithm starts. This provides initial conditions for the optimization loop. Loops marked as "optimized" are executed periodically, such as daily, monthly, or yearly. When executed, the loop is run as many times as necessary to achieve good enough convergence of the optimization process. After convergence, a new set of outputs is sent to the wind turbine controller (x1) and the operator (other outputs) to implement the determined control strategy. The two blocks "Calculate estimates of LCoE" contain the same calculation method. They include all the elements not yet fixed in Figure 6, namely O&M costs, capacity factors, availability and longevity. For example, the tower CAPEX is already fixed, so it doesn't need to be included. But operation and maintenance (O&M) costs are not fixed, as the gearbox can work harder and be replaced once during the life of the turbine, so this is included.

Where there are many similar connections, eg the connection between the optimization algorithm block and the block "Compute Estimates of LCoE", not all connections in Figure 7 are shown. The following terms are used in or with reference to Figure 7:

- The number of time periods (eg, years) of N remaining life. If desired, the user can change this to suit his desired operating strategy.

x1 For example, for 3MW turbines [3.5MW, 3.49MW, 3.49MW, 3.48MW, 3.47MW...], a one-dimensional array of individual wind turbine maximum power levels in 1...N years,

A 1D array of the number of gearbox replacements for x2 in years 1...N, eg [0,0,0,0,0,0,0,0,1,0,0,0,0,0]

A one-dimensional array of the number of generator replacements for x3 in years 1...N

A one-dimensional array of the number of main bearing replacements for x4 in 1...N years

A 1D array of the number of blade set replacements for x5 in 1...N years and optionally:

A 1D array of the number of converter replacements for x6 in 1...N years

1D array of pitch bearing replacement times for x7 in 1...N years

1D array of x8 pitch actuator replacement times (hydraulic or electrical) in 1...N years

A one-dimensional array of the number of yaw drive replacements for x9 in 1...N years

A one-dimensional array of x10 yaw bearing replacement times in 1...N years

A 1D array of transformer replacement times for x11 in years 1...N

● "_0" represents the initial condition, for example x1_0 is the initial condition of x1

Referring to Figure 7, the optimization process entails determining a number of constants for a given turbine and calculating initial conditions for optimization using the values of a number of physical and control parameters. Once the initial conditions are calculated, the optimization process applies a function that defines the relationship between the leveled energy cost and the input values of the physical and control parameters to determine the level that minimizes the leveled energy cost without exceeding certain optimization constraints combination of input values.

In order to calculate the optimal initial conditions, a number of parameter values for a given turbine are determined and entered into an "initialization" block. These values are constant for any given periodic optimization (eg, monthly). They are operator-entered parameters and can be changed at any time, but if changed, they will be applied the next time the optimization is run. These parameters may include one or more of the following: Turbine/Individual Turbine Component Life; Gearbox Replacement Cost; Bearing Replacement Cost; Generator Replacement Cost; Blade Replacement Cost; Pitch System Replacement Cost; Replacement cost of any other parts.

The life of the turbine and/or the life of one or more components may be determined, for example, using a site inspection function and/or one or more LUEs, or may be provided as constraints to be satisfied. Replaceable parts include blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, yaw drives, yaw bearings or transformers.

Determine the total cost of replacing each part. For example, for replacing a gearbox, the cost will take into account whether to install a new gearbox or a refurbished gearbox, transportation costs, and crane and labor costs. Turbine downtime costs are also included in Figure 6 under the Availability section.

Other costs may be included, such as financial costs (including weighted average cost of capital (WACC), etc.), as well as other elements required to calculate the impact of future wind turbine operating strategies on LCoE.

The lifetime parameters may be set by the operator according to its operating strategy for the site, or may be determined as part of optimization. The other constants are based on best knowledge, so they may be updated occasionally, but such updates will be fairly rare. Specifically, O&M costs can only be estimated in advance, and over time these estimates will be replaced by actual data, resulting in more accurate estimates of future O&M costs.

The "initialization" block and optimization algorithm use the following variables:

x1 eg for 3MW turbines [3.5MW, 3.49MW, 3.49MW, 3.48MW, 3.47MW...], a one-dimensional array of maximum power levels in 1...N years,

A 1D array of the number of gearbox replacements for x2 in years 1...N, e.g. [0,0,0,0,0,0,0,0,1,0,0,0,0,0]

x3 1D array of generator replacements in 1...N years

A 1D array of the number of main bearing replacements for x4 in 1...N years

x5 1D array of blade set replacement times in 1...N years and optionally:

A 1D array of the number of converter replacements for x6 in 1...N years

1D array of pitch bearing replacement times for x7 in 1...N years

A 1D array of the number of pitch actuator replacements (hydraulic or electrical) for x8 in 1...N years

A 1D array of the number of yaw drive replacements for x9 in 1...N years

x10 1D array of yaw bearing replacement times in 1...N years

x11 1D array of transformer replacement times in 1...N years

The initial calculation of the estimate of LCoE uses the operator's initial estimate of the initial conditions, x1_0, x2_0, x3_0, etc.

The signal labeled "Measured Data" in Figure 7 contains data from the sensors and data determined from the O&M process. Measurements from sensors can come from turbines or wind farms and can include one or more of the following:

- one or more of turbine components such as gearboxes, generators, main bearings, blades, converters, pitch bearings, pitch actuators (hydraulic or electric), yaw drives, yaw bearings, transformers LUE value of each component;

- wind speed and environmental data, or other data obtained from site inspection procedures;

- CMS data for one or more of the turbine components.

Measurements from operations and maintenance (O&M) activities include O&M costs, which may include estimates based on costs to date, if any. This is used in conjunction with future predetermined service patterns, experience with other turbines of the same design from the same or other wind farms, and experience with certain components of other turbines of different designs using the same components, to give Estimates of future O&M costs.

Depending on the initial conditions, the optimization process uses inputs and constraints to minimize the levelized energy cost (LCoE) by directly computing the LCoE or by computing some LCoE variables. Only the portion of the LCoE that changes after the turbine is built needs to be calculated, i.e. the portion affected by O&M costs, capacity factors, availability and lifetime. The optimization is run until the LCoE is minimized, eg, until the calculated stepwise change in LCoE is within a given tolerance.

Constraints on optimization are regions that the optimization algorithm cannot enter when searching for the minimum value of LCoE. The constraints may include one or more of the following: wind turbine type maximum power level; turbine type minimum power output; maximum active power capacity of the wind farm connected to the grid, i.e. the maximum sum of the active power outputs of the turbines; and any other appropriate constraints.

Constraints can also include one or more of the following, which can be user-defined:

- Minimum or target expected wind turbine life;

- the maximum number of part replacements for all parts or for one or more given parts;

- A predefined maximum power level arrangement, or a predefined relative maximum power arrangement that defines the shape of the maximum power arrangement.

The number of inputs to each 1D array can be chosen to make the runtime of the optimization algorithm more manageable. The one-dimensional arrays x1, x2, etc. are described above as being provided for each year of operation. This would give 12x or 4x the input, although it is possible to provide input for each month or season run. So annual values can be used. Of course, different time periods may be used as appropriate, depending on the desired computation time or optimized interval.

Also, to make runtime more manageable, wind turbine components can be selected such that only the most relevant components are used in optimization. The components to be included, in particular gearboxes, generators, main bearings and blades, can be selected based on whether their lifespan is significantly affected by active power output above rated wind speed.

Additionally or alternatively, components used in optimization may be selected based on their values. For example, only components having a value of 5% or more of the total cost of the turbine may be included.

The optimizer algorithm produces multiple outputs each time it runs to converge. A one-dimensional array x1 representing the arrangement of the maximum power levels of the turbines in 1...N years can be used for closed loop control by automatically transferring data to the wind turbine controller for use according to the turbine power demand until the next time Run the optimization loop (eg, after 1 month). Alternatively, the maximum power level may be used without an automatic control loop at the proposed capacity, eg, by sending maximum power level data to a computer system for output on a display for viewing by a service department.

The other one-dimensional arrays x2, x3, x4 represent the arrangement of parts replacement. This scheduling data can be exported to another computer system to allow action to be taken. Data can be fed directly into parts replacement scheduling software. Alternatively, parts replacement data including proposed replacement dates may be used as a recommendation output that is sent to a display for review by service to determine manual implementation of the parts replacement program.

It should be noted that the one-dimensional array (x1) of maximum power levels described above can be provided as over-rated levels only, over-rated or de-rated levels, or only de-rated levels such that the maximum power Flat variables only need to specify the amount above (or below) the rated power. The power demand may alternatively be the speed demand and/or torque demand per cycle, or fatigue life consumption in the case of power being controlled through the life usage control function as described below. The disadvantage of using both speed demand and torque demand is that the computation time to calculate the optimal configuration will be longer.

Although the optimizer is described above as being executed periodically, it can be used occasionally, or even only once. For example, optimization can be performed offline at the point where the over-rated controller is installed. Alternatively, the optimizer may be embodied in the controller of the wind turbine, wind power plant or elsewhere, in which case it will be executed at specific time steps.

As discussed above, optimization can be performed with or without LUEs, as site data can be used to determine component fatigue and thus give an indication of the remaining life available to the turbine or turbine components.

While the optimization algorithm is primarily described as involving use with an over-rated controller, this is not a requirement. This optimization can be applied with any control action that trades off energy capture for turbine fatigue loads. This may include one or more of: changing power requirements, such as by derating; thrust limiting, which limits power output by reducing rotor thrust at the "knee" of the power curve at the expense of power output to prevent high thrust load; or any other control feature that compromises energy capture and fatigue loading.

While the required calculations can be performed anywhere, in practice such strategic actions can be better performed in a wind farm controller such as a SCADA server. This allows service data to be entered directly in the field, avoiding communication problems from site to control center. However, calculations can also be performed at the control center. Other methods described herein, including the method of the first example, are equally applicable.

- Maximum power level calculation

Exemplary techniques for determining a maximum power level applicable to a turbine are now described next.

A method for determining a wind turbine type maximum power level for a type of wind turbine may include simulating a load spectrum for two or more test power levels to determine for the two or more tests the load on the type of wind turbine for each of the power levels; comparing the determined load for each test power level to the design load for the type of wind turbine; and the wind turbine for the type of wind turbine The turbine type maximum power level is set to the maximum test power level at which the determined load does not exceed the design load for that type of wind turbine.

Thus, a wind turbine type maximum power level may be determined for one or more types of wind turbines.

FIG. 8 shows a flowchart detailing an example of setting a maximum power level for a turbine that may be used by any of the embodiments. In step 301, checks are performed to determine wind turbine mechanical component design limits for one or more types of wind turbines. In this example, an offline computer system was used to determine design limits. However, as will be appreciated, this functionality may be implemented by an online computer system or any other software and/or hardware associated with the wind turbine and/or WPP.

The wind turbine type maximum power level is the maximum power level that a given type of wind turbine is allowed to produce when the wind is suitably high if the wind turbine of the given type operates within the limits of the design loads of the components of the wind turbine. The wind turbine type maximum power level effectively applies to the design life of the turbine. Therefore, the wind turbine type maximum power level will typically be higher than the nominal nameplate rating for that type of wind turbine, as the nominal nameplate rating is usually a more conservative value.

One type of wind turbine used in the following examples and embodiments may be understood as a wind turbine having the same electrical systems, mechanical systems, generators, gearboxes, turbine blades, turbine blade lengths, hub heights, and the like. Thus, for purposes of embodiments of the present invention, any difference from the main structure or components of the wind turbine effectively results in a new type of wind turbine. For example, the same wind turbine, except that the hub height (eg, tower height) is different, would be two different types of wind turbines. Similarly, the same wind turbine, except that the lengths of the turbine blades are different, would also be considered two different types of wind turbines. Additionally, 50Hz and 60Hz wind turbines are considered to be different types of wind turbines, cold climate and hot climate designed wind turbines.

This type of wind turbine therefore does not necessarily correspond to an International Electrotechnical Commission (IEC) class of wind turbines, as different types of turbines can belong to the same IEC class of wind turbines, where each type of wind turbine can have a different wind turbine type maximum Power levels, based on wind turbine design and components.

In the example below, the wind turbine is rated at a nominal nameplate rated power level of 1.65MW (1650KW), with a hub height of 78 meters, and is designed for service at specific IEC wind class conditions.

The design of wind turbine type mechanical components can then be determined for this type of wind turbine by simulating the load spectrum for a first test over-rated power level to identify the loads on that type of wind turbine for that first power level limit. The loads may be mechanical loads, fatigue loads, any other loads that the wind turbine may experience, or any combination of different loads. However, as will be appreciated, in this example, mechanical loads are considered to be other loads, such as fatigue loads, which may also be considered. The process of simulating the load spectrum may also include or may be performed to determine inferences or other forms of analysis of the loads on the type of wind turbine.

The load spectrum typically includes a series of different test cases that can be run in a computer simulation of the wind turbine. For example, the load spectrum may include test cases with wind speed of 8m/s for 10 minutes, test cases with wind speed of 10m/s for 10 minutes, test cases with different wind directions, test cases with different wind turbulence, wind turbine startup Test cases, test cases with wind turbine shutdown, etc. It should be appreciated that there are many different wind speeds, wind conditions, wind turbine operating conditions, and/or fault conditions for which there are test cases to be run in a wind turbine simulation of the load spectrum. Test cases may include real, actual data, or artificial data (eg, 50-year high winds as defined in standards related to wind turbines). Simulation of the load spectrum can determine the forces and loads affecting the wind turbine for all test cases in the load spectrum. The simulation can also estimate or determine the number of possible occurrences of a test case event, for example, a test case of 10 m/s wind with a duration of 10 minutes might be expected to occur 2000 times over the 20-year lifetime of the wind turbine, and can therefore calculate Fatigue of a wind turbine during its lifetime. The simulation may also calculate or determine fatigue damage or loads that may be caused by various components in the wind turbine based on the determined loads affecting the wind turbine.

In this example, the first test power level may be 1700KW, as this is higher than the nominal nameplate rating power level for the type of wind turbine considered in this example. The load spectrum can then be simulated for a given type of wind turbine in order to determine whether that type of wind turbine can operate at the first test power level without exceeding the ultimate design loads of mechanical components of that type of wind turbine. If the simulation determines that the type of wind turbine can operate at the first test power level, the same process can be repeated for the second test power level. For example, in this example, the second test power level may be 1725KW. The load spectrum is then simulated for a given type of wind turbine to determine whether that type of wind turbine can operate at this second test power level without exceeding the limit design loads of the mechanical components.

The process of simulating the load spectrum for additional test power levels may be performed iteratively if the ultimate design load of the mechanical component is not exceeded. In this example, the test power level is incremented in 25KW steps, however, it should be understood that the incremental steps may be any suitable step size for the purpose of determining the maximum power level for the type of wind turbine, for example, 5KW , 10KW, 15KW, 20KW, 30KW, 50KW, etc., or the test power level can be increased by a percentage of the test power level, eg, 1% increments, 2% increments, 5% increments, etc. Alternatively, the process starts with a high first test power level, and for each iteration, the test power level is decremented by a suitable amount until the maximum power level for the type of wind turbine is determined, i.e. the type of wind turbine A first test power level at which operation can be performed without exceeding the extreme design limit.

In this example, a given type of wind turbine is determined to be capable of operating at additional test power levels of 1750KW, 1775KW and 1800KW before exceeding the design limits of one or more mechanical components at 1825KW. Therefore, the process determines that the wind turbine type maximum power level for this type of turbine is 1800KW.

In a further embodiment, the process may also be iterated in smaller increments as this type of wind turbine does not exceed the ultimate design load of mechanical components at 1800KW, but exceeds the ultimate design load of mechanical components at 1825KW Power levels are incrementally tested, eg, 5KW, to determine whether the wind turbine can operate at power levels between 1800KW and 1825KW without exceeding the mechanical limit design loads. However, in the current example, a power level of 1800 KW is considered to be the wind turbine type mechanical component design limit for this type of wind turbine.

The process of determining the wind turbine type maximum power level may then be performed for any other type of wind turbine to be analyzed. In step 302 of Figure 8, design constraints for electrical components in a wind turbine of this type may be considered or evaluated against previously determined wind turbine mechanical component design constraints.

In step 302, the main electrical components may be considered to ensure that the determined wind turbine type power level for mechanical component design limits does not exceed the design limits of the main electrical components of the type of wind turbine being analyzed. The main electrical components may include, for example, generators, transformers, internal cables, contactors, or any other electrical components in a wind turbine of this type.

Based on the simulations and/or calculations, it is then determined whether the main electrical components can operate at the wind turbine type maximum power levels previously determined for mechanical component design limits. For example, operation at mechanical component design limit power levels may increase the temperature of one or more cables inside the wind turbine and thus reduce the cable current carrying capacity, as determined by the size and heat dissipation conditions of the cable conductors. Therefore, the current carrying capacity will be calculated for the new temperature conditions in order to determine whether the cable can operate at power levels up to the wind turbine type maximum power level. Similar reasons may be considered for other electrical components, eg, temperature of the component, capacity of the component, etc., to determine whether the electrical component can operate at power levels up to the design limit of the mechanical component.

If it is determined or determined that the primary electrical components are capable of operating within the previously determined mechanical component design limits, then in step 303 of Figure 8, for a given type of wind turbine, the wind turbine type maximum power level is set according to the mechanical component design limits Or recorded as the maximum power level for that given type of wind turbine. However, if it is determined or determined that the primary electrical components cannot operate within the previously determined mechanical component design limits, further investigation or action may also be undertaken to derive turbine-type maximum power levels that accommodate both mechanical and electrical components.

Once the wind turbine type maximum power level has been determined for each type of wind turbine, this parameter can be used as a constraint within the method described above to derive an individual maximum power level for each wind turbine in the WPP flat arrangement, for example, the maximum over-rated power level.

A different individual maximum power level for each wind turbine in a WPP is advantageous because conditions in a WPP may vary throughout the site of the WPP. Thus, it may be the case that a wind turbine at one location in the WPP may face different conditions than another wind turbine of the same type at a different location in the WPP. Therefore, two wind turbines of the same type may require different individual maximum power levels, or depending on the preferred embodiment, the lowest individual maximum power level may be applied to all wind turbines of that type in the WPP. As described herein, individual wind turbine specific individual maximum power levels are determined as part of the determination of the schedule.

- Over rated control

Embodiments of the present invention may be applied to wind turbines or wind power plants that operate by applying an over-rating control to determine the amount of over-rating to apply.

An overrating control signal is generated by the overrating controller and used by the wind turbine controller to overrate the turbine. The control arrangement described above can be used within or with such an over-rating controller to set an upper limit on the amount of power that can be generated by over-rating. The specific manner in which the over-rated control signal is generated is not critical to embodiments of the present invention, but an example will be given for ease of understanding.

Each wind turbine may include an over-rated controller as part of the wind turbine controller. An over-rating controller calculates an over-rating request signal that indicates an amount by which the turbine is configured to over-rate to a power output above the rated output. The controller receives data from turbine sensors, such as pitch angle, rotor speed, power output, etc., and can send commands, such as setpoints for pitch angle, rotor speed, power output, and the like. The controller may also receive commands from the grid, eg, from a grid operator, to increase or decrease real or reactive power output in response to grid demand or failures.

Figure 9 shows a schematic example of a turbine controller arrangement wherein an over-rating controller 901 generates an over-rating control signal that can be used by the wind turbine controller to apply over-rating to the turbine. The over-rated control signal may be generated depending on the output of one or more sensors 902/904 that detect operating parameters of the turbine and/or local conditions such as wind speed and direction. Overrating controller 901 includes one or more functional control modules that can be used for various aspects of overrating control. Additional functional modules may be provided, the functionality of modules may be combined, and certain modules may be omitted.

The optimizer 907 provides values for individual turbine maximum power levels according to the determined schedule as described herein. By arrangement, this provides the maximum power level at which the turbine can operate.

Additional functional modules generate power requirements and are typically used to reduce final power requirements acted upon by the turbine controller. A specific example of an additional functional module is the operational constraints module 906 . Over-rated utilization typically exists as a gap between the component design loads and the loads experienced by each turbine in operation, which is generally better than the IEC standard simulation conditions under which the design loads are calculated. Overrating causes the power demand of the wind turbine to increase in strong winds until an operating limit specified by operating constraints (temperature, etc.) is reached, or until a power ceiling is reached that is set to prevent exceeding component design loads. The operational constraints implemented by the operational constraints control module 906 limit possible over-rated power requirements based on various operational parameters. For example, where the protection function properly initiates a shutdown when the gearbox oil temperature exceeds 65°C, the operating constraints may be based on the gearbox oil temperature exceeding 60°C, at 65°C "impossible to overrate" (i.e., power setpoint signal equal to rated power), indicating the maximum possible linear drop of the overrated setpoint signal.

The minimum functional block 908 is provided with the maximum power levels and power requirements of the functional modules, and the minimum value is selected. An additional minimum block 909 may be provided which selects the minimum power demand from the over-rated controller 901 and any other turbine power demand such as specified by the grid operator for generating the final power demand imposed by the wind turbine controller.

Alternatively, for example, the over-rated controller may be part of the PPC controller 130 of Figure IB. The PPC controller is in communication with each of the turbines and can receive data from the turbines, such as pitch angle, rotor speed, power output, etc., and can send commands to the individual turbines, such as pitch angle, rotor speed, power output etc. set point. The PPC 130 also receives commands from the grid, such as commands from the grid operator to increase or decrease real or reactive power output in response to grid demand or failures. The controller of each wind turbine communicates with the PPC 130 .

The PPC controller 130 receives power output data from each of the turbines and thus knows the power output by each turbine and power plant as a whole at the grid connection point 140 . If desired, the PPC controller 130 may receive an operating set point for the power output of the power plant as a whole and divide it among each turbine so that the output does not exceed the operator specified set point. The plant setpoint can be any value from 0 to the rated power output of the plant. The "rated power" output of a power station is the sum of the rated power outputs of the individual turbines within the power station. The plant set point may be higher than the rated power output of the plant, ie the entire plant is overrated.

The PPC can receive input directly from the grid connection, or it can receive a signal that is a measure of the difference between the total power plant output and the nominal or rated power plant output. This difference can be used to provide a basis for overrating the individual turbines. In theory, only a single turbine can be overrated, but it is preferable to overrate multiple turbines, and most preferably to send an overrating signal to all turbines. The over-rating signal sent to each turbine may not be a fixed control, but may be an indication of the maximum amount of over-rating that each turbine may perform. Each turbine may have an associated controller, which may be implemented within the turbine controller or centrally at the PPC, for example, that will implement one or more of the functions shown in Figure 9 to determine whether the turbine is capable of responding The over-rated signal, and if the over-rated signal can be responded to, the amount of the over-rated signal is determined. For example, where the controller within the turbine controller determines that conditions at a given turbine are favorable and above rated wind speed, it may respond positively and the given turbine is overrated. Since the controller implements the over-rated signal, the output of the power station will rise.

Thus, an over-rating signal is generated, either centrally or at each individual turbine, indicating the amount of over-rating that may be performed by one or more turbines or the turbines of the power plant as a whole.

-Lifetime usage estimator

As described above, embodiments of the present invention utilize a lifetime usage estimator (LUE). The lifetime usage estimator will now be described in more detail. Algorithms required to estimate life usage vary for different parts, and LUE may include a library of LUE algorithms that includes some or all of the following: Load Duration, Load Rotation Distribution, Rainflow Counts, Stress Cycling Damage, Temperature Cycling Damage, generator thermal response rate, transformer thermal response rate and bearing wear. Additionally other algorithms may be used. As mentioned above, lifetime usage estimates are only available for selected critical components, and selection of a new component for the LUE and selection of an appropriate algorithm from the library and specific parameters set for that component are implemented using a library of algorithms.

In one embodiment, the LUE is implemented for all major components of the turbine, including blades; pitch bearings; pitch actuators or drives; hubs; main shafts; main bearing housings; main bearings; gearbox bearings; gear teeth ; generators; generator bearings; converters; generator junction box cables; yaw drives; yaw bearings; towers; offshore support structures, if present; foundations; and transformer windings. Alternatively, one or more of these LUEs may be selected.

As examples of suitable algorithms, rainflow counts can be used in blade structures, blade bolts, pitch systems, main shaft systems, converters, yaw systems, towers, and foundation estimators. In the blade structure algorithm, rainflow counts are applied to the blade root bending wing direction (flapwise) and edgewise (edgewise) moments to determine the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. For blade bolts, rainflow counts are applied to the bolt bending moment to determine the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. In the pitch system, main shaft system, tower and foundation estimator, the rainflow counting algorithm is also used to determine the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. Parameters for applying the rainflow algorithm can include:

- pitch system - pitch force;

- Spindle system - Spindle torque;

- tower-tower stress;

- Foundation - Foundation stress.

In the yaw system, the rainflow algorithm is applied to the tower top torque to identify the load duration, and this output is sent to the stress cycle damage algorithm. In the converter, generator power and RPM are used to infer temperature, and rainflow counts are used over that temperature to identify temperature cycles and averages.

The lifetime usage of the blade bearings can be monitored by entering the blade airfoil directional load and pitch speed as input to the load duration algorithm or to the bearing wear algorithm. For gearboxes, apply the load rotation duration to the spindle torque to calculate the life used. For generators, generator RPM is used to infer generator temperature, which is used as input to the generator thermal response rate algorithm. For transformers, the transformer temperature is inferred from power and ambient temperature to provide input to the transformer thermal response rate algorithm.

Where possible, existing sensors are preferably used to provide input to the algorithm for its operation. Thus, for example, wind turbines often directly measure the blade root bending flange direction and wing direction moments required by the blade structure, blade bearings and blade bolt estimators. For a pitch system, the pressure in the first chamber of the cylinder can be measured and the pressure in the second chamber can be deduced, enabling the calculation of the pitch force. These are only examples, other parameters required as input can be measured directly or inferred from other available sensor outputs. For some parameters, it may be advantageous to use additional sensors if the value cannot be inferred with sufficient accuracy.

Algorithms for various types of fatigue assessments are known and can be found in the following standards and texts:

Load rotation distribution and load duration:

Wind Turbine Certification Guidelines, Germainischer Lloyd, Section 7.4.3.2 Fatigue Loads

Rainflow:

IEC 61400-1 Wind Turbines - Part 1: Design Requirements, Annex G

Miners sum:

IEC 61400-1 Wind Turbines - Part 1: Design Requirements, Annex G

Power law (chemical decay):

IEC 60076-12 "Power transformers - Part 12: Loading guidelines for dry-type power transformers", section 5.

- Power station level control

Any of the methods described herein may be performed at the wind power plant level, thereby generating plant control arrangements including individual control arrangements for each wind turbine. This has the benefit of allowing interactions between turbines in a given power plant to be taken into account.

Changes in the power demand/power level of the one or more upstream turbines affect the power output and fatigue damage accumulation rate of any turbine following the one or more upstream turbines. The site inspection software includes information on the positioning of the turbines within the wind farm and takes into account the relative positions of the turbines within the wind farm relative to each other. The wake effect of the upstream turbine is therefore taken into account by the site inspection software.

In the case of some wind power plants, the power carrying capacity of the connection from the power plant to the utility grid is less than the sum of the power generated by each turbine if all turbines are generating power at the wind turbine type maximum power level. In this case, the control arrangement of the wind turbines or wind power plants is also constrained such that for any given period of time within the arrangement, when the power of all turbines is added together, it does not exceed the amount of power from the power plant to the grid The amount of power that can be carried in the connection.

Embodiments described herein rely on analysis of turbine characteristics and turbine site characteristics to determine a control arrangement for the turbine. Various calculations, including those performed by the site inspection software, may be performed off-line at one or more different computing systems, and the resulting control arrangements provided to the wind turbine or power plant controller. Alternatively, the calculations may be performed online at the wind turbine controller or the power plant controller.

The embodiments described above are not exclusive and one or more of the features may be combined or may cooperate to achieve improved over-rating control by setting a maximum power level for each wind turbine in a wind power plant , which takes into account the environmental and site conditions that the wind turbine faces or affects.

It should be noted that embodiments of the present invention can be applied to both constant speed and variable speed turbines. Turbines may employ active pitch control whereby power limitations above rated wind speed are achieved by feathering, which involves rotating all or part of each blade to reduce the angle of attack. Alternatively, the turbine may employ active stall control, which achieves a power limitation above rated wind speed by pitching the blades to stall (opposite the direction of active pitch control).

While embodiments of the present invention have been shown and described, it will be understood that these embodiments have been described by way of example only. Numerous modifications, changes and substitutions will occur to those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims (34)

What is claimed is: 1. A method of generating a control schedule for a wind turbine, the control schedule indicating how a turbine maximum power level changes over time, the method comprising: determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data; Applying an optimization function that alters an initial control arrangement to determine an optimization by altering the trade-off between fatigue life consumed by the turbine or the one or more turbine components and energy capture until an optimized control arrangement is determined The control arrangement, the optimization includes: estimating, based on the current remaining fatigue life and the changed control schedule, a future fatigue life to be consumed by the turbine or the turbine component for the duration of the changed control schedule; and Constraining optimization of the control arrangement according to one or more input constraints; wherein the input constraints include a maximum allowable number of component replacements for one or more turbine components, and the optimizing further includes changing an initial value of wind turbine life to determine a target wind turbine life. 2. The method of claim 1, further comprising: The control schedule is optimized by changing the timing of component changes and changing the number of component changes until a maximum number is reached. 3. The method of claim 1 or 2, wherein the one or more turbine components that can be replaced include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts , main bearings, gearboxes, generators, converters, yaw drives, yaw bearings or transformers. 4. The method of claim 1 or 2, wherein the initial control schedule specifies a relative change over time of the maximum power level of the turbine at which the turbine can operate. 5. The method of claim 1 or 2, wherein the input constraints further comprise an upper limit maximum power output of the turbine and/or a minimum power output of the turbine allowed by the turbine design. 6. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises combining sensors from one or more turbine sensors The data is applied to one or more lifetimes using an estimation algorithm. 7. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises using data from a condition monitoring system. 8. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises using, in conjunction with a site inspection procedure, data from wind farm sensors From the acquired data, the site inspection program determines loads acting on turbine components based on the wind farm sensors and parameters related to the wind farm and the wind turbine design. 9. The method of claim 1 or 2, wherein the optimization of the control arrangement comprises: The control arrangement is changed to minimize the levelized energy cost. 10. The method of claim 9, wherein the levelized energy cost is determined using a levelized energy cost model, the model comprising parameters of one or more of the following: a capacity factor indicating the energy generated during a time period divided by the energy that could be generated if the turbine were to operate continuously at rated power during that time period; availability, indicating when the turbine can be used to generate electricity; and Field Efficiency, indicating the energy generated over a period of time divided by the energy that could be generated if the turbine were operating in wind that was completely undisturbed by the upstream turbine. 11. The method of claim 10, wherein the model further comprises parameters of one or more of the following: Costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and transportation costs for refurbished components or said replacement components to the power station; and Service costs associated with replacing worn components. 12. The method of claim 1 or 2, wherein the optimized control arrangement is an arrangement for the maximum power level at which the turbine can operate. 13. The method of claim 1 or 2, wherein the control arrangement indicates an amount of fatigue damage that should be caused over time, the method further comprising operating the wind turbine based on one or more lifetime usage estimators Fatigue damage is induced at a rate indicated by the control arrangement. 14. The method of claim 1 or 2, wherein the control arrangement specifies a maximum power level above the rated power of the wind turbine. 15. The method of claim 1 or 2, wherein the control arrangement dictates how the turbine maximum power level changes over the life of the turbine. 16. The method of claim 1 or 2, further comprising providing the optimized control arrangement to a wind turbine controller or a wind power plant controller to control the power output of the wind turbine. 17. The method of claim 1 or 2, wherein the method is repeated periodically. 18. The method of claim 17, wherein the method is repeated daily, monthly or yearly. 19. A controller for a wind turbine or wind power plant, the controller being configured to perform the method of any one of claims 1 to 18. 20. An optimizer for generating a control schedule for a wind turbine, the control schedule indicating how a turbine maximum power level changes over time, the optimizer comprising: an optimization module configured to receive: initial values for a set of variables that are operating variables of the wind turbine and include initial control arrangements; one or more constraints; and an indication of the turbine or a or data on the current remaining fatigue life of multiple turbine components; Wherein, the optimization module is configured as: The optimization is performed by changing one or more of the variables from their initial values based on the remaining fatigue life of the turbine or the one or more turbine components and the one or more constraints. maximizing or minimizing operating parameters received at the module depending on the set of variables to optimize the control arrangement; and Output optimized control arrangements; Wherein the constraints include a maximum allowable number of component replacements for one or more turbine components, and the optimization module is further configured to vary the initial value of wind turbine life to determine a target wind turbine life. 21. The optimizer of claim 20, further comprising an initialization module configured to receive sensor data and initial values for the set of variables, the initialization module configured to calculate initial values for the operating parameters value. 22. An optimizer according to claim 20 or 21, wherein the one or more turbine components are one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main shafts Bearings, gearboxes, generators, converters, yaw drives, yaw bearings or transformers. 23. The optimizer of claim 20 or 21, wherein the operating parameter is a levelized energy cost of the turbine, and optimizing the control arrangement comprises minimizing the levelized energy cost. 24. The optimizer of claim 23, wherein the levelized energy cost is determined using a levelized energy cost model, the model including parameters of one or more of the following: a capacity factor indicating the energy generated during a time period divided by the energy that could be generated if the turbine were to operate continuously at rated power during that time period; availability, indicating when the turbine can be used to generate electricity; and Field Efficiency, indicating the energy generated over a period of time divided by the energy that could be generated if the turbine were operating in wind that was completely undisturbed by the upstream turbine. 25. The optimizer of claim 24, wherein the model further comprises parameters of one or more of the following: Costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and transportation costs for refurbished components or said replacement components to the power station; and Service costs associated with replacing worn components. 26. A controller comprising an optimizer according to any of claims 20 to 25. 27. A wind turbine comprising the controller of claim 26. 28. A wind power plant comprising the controller of claim 26. 29. A method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating, for each wind turbine, how a maximum power level varies over time, the method comprising: determining a value indicative of the current remaining fatigue life of each of the plurality of turbines or one or more components of each of the plurality of turbines based on the measured wind turbine site data and/or operational data; Applying an optimization function by varying the relationship between fatigue life consumed and energy capture for each of the plurality of turbines or the one or more turbine components of each of the plurality of turbines to change the initial control schedule for each of the plurality of turbines to determine an optimized control schedule until an optimized control schedule is determined, the optimization comprising: Using a site inspection procedure based on the current remaining fatigue life and the changed control schedule to estimate future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule, the site inspection The program determines loads acting on turbine components based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design, and includes loads between the plurality of turbines of the wind power plant. interact; and Constraining optimization of the control arrangement according to one or more input constraints; wherein the constraints include a maximum allowable number of component replacements for each of the one or more turbine components of each of the plurality of wind turbines, and the optimization function is further applied to change the wind The initial value of the turbine life determines the target wind turbine life. 30. The method of claim 29, wherein the data obtained from wind power plant sensors comprises sensor data collected prior to commissioning and/or construction of the wind turbine or the wind power plant. 31. A method according to claim 29 or 30, wherein the optimization function varies, for one or more of the turbine components, the number of times that component can be replaced during the remaining life of the turbine. 32. The method of claim 31, wherein the optimization function makes for one or more of the turbine components when the component can be replaced during the remaining life of the turbine Change. 33. A method according to claim 29 or 30, wherein the method is further constrained such that for any given period of time within the arrangement, when the powers of all the turbines are added together, the power and will not exceed the amount of power that can be carried in the connection from the power station to the grid. 34. A wind power plant controller configured to perform the method of any one of claims 29 to 33.

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