CN116181584B - A method and system for identifying extreme turbulence characteristics of wind condition distribution at a wind turbine station - Google Patents
A method and system for identifying extreme turbulence characteristics of wind condition distribution at a wind turbine station Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/022—Adjusting aerodynamic properties of the blades
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
本发明属于风力机组湍流识别技术领域，涉及一种风力机组场站风况分布极端湍流特征识别方法，包括以下步骤：获取目标风电场各机位点机组的历史运行数据；对历史运行数据进行数据清洗，得到符合要求的风速数据；对符合要求的风速数据进行校正；选取满足连续性的风速数据，且按照预设时间间隔进行分割，分割成N组；计算每个预设时间间隔的平均风速和平均湍流，得到实测风速湍流曲线；将实测风速湍流曲线和IEC标准风速湍流曲线对比，超出IEC标准散点数量进行累积，并与总数量N进行对比，其比值作为风场风况复杂程度的定量评估值。通过各机位点实际运行的风况超出设计风况的比例来确定与设计是否相符，并根据比例来确定是否实施控制干预。
The present invention belongs to the technical field of wind turbine turbulence identification, and relates to a method for identifying extreme turbulence characteristics of wind condition distribution in a wind turbine station, comprising the following steps: obtaining historical operation data of units at each station of a target wind farm; cleaning the historical operation data to obtain wind speed data that meets the requirements; correcting the wind speed data that meets the requirements; selecting wind speed data that meets continuity, and segmenting them according to preset time intervals into N groups; calculating the average wind speed and average turbulence of each preset time interval to obtain a measured wind speed turbulence curve; comparing the measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, accumulating the number of scattered points that exceed the IEC standard, and comparing it with the total number N, and the ratio is used as a quantitative evaluation value of the complexity of the wind condition in the wind farm. Whether the wind condition actually operated at each station exceeds the design wind condition is determined by the proportion of the wind condition actually operated at each station exceeding the design wind condition, and whether to implement control intervention is determined according to the proportion.
Description
Technical Field
The invention belongs to the technical field of wind turbine generator turbulence identification, and particularly relates to a wind turbine generator station wind condition distribution extreme turbulence characteristic identification method and system.
Background
With the annual improvement of the wind power generation installation quantity, a considerable number of units are installed in mountain areas and hilly areas, compared with plain areas, the wind condition characteristics of the terrains are more complex, the characteristics of high wind, high turbulence and the like exist, fatigue load and limit load overrun of the units are easy to cause, IEC standards have clear requirements on wind conditions in unit design input, the value ranges of turbulence under different wind speeds need to be defined according to different wind areas, and manufacturers can design the units according to the design wind condition curves of reference wind areas.
However, the actual unit operation wind condition may have a larger difference from the theoretical wind condition curve specified in the IEC standard, which is an important cause of damage to components due to the over-limit of unit load, but no quantitative analysis and visualization display of the wind condition characteristics of the designed wind region based on a big data analysis method are available in the related literature at present.
Disclosure of Invention
The invention aims to provide a wind turbine station wind condition distribution extreme turbulence characteristic identification method and system, which solve the problem that quantitative analysis and visualization display of wind condition characteristics of a designed wind area are not performed at present.
The invention is realized by the following technical scheme:
A wind turbine station wind condition distribution extreme turbulence characteristic identification method comprises the following steps:
s1, acquiring historical operation data of each machine position unit of a target wind power plant, wherein the historical operation data comprise wind speeds;
S2, performing data cleaning on the acquired historical operation data to obtain wind speed data meeting the requirements;
S3, correcting wind speed data meeting requirements according to the ambient temperature and the on-site altitude, and ensuring that the wind speed data is compared with an IEC design curve on the same standard;
S4, selecting wind speed data meeting continuity from data meeting requirements, and dividing the wind speed data meeting continuity into N groups according to preset time intervals;
Calculating the average wind speed of each preset time interval to obtain N average wind speeds;
s5, calculating average turbulence corresponding to the preset time intervals according to the average wind speed of each preset time interval to obtain N average turbulence;
s6, sorting the data of the S4 and the S5 to obtain N groups of scattered point data sets of average wind speed and average turbulence, and obtaining an actually measured wind speed turbulence curve;
S7, comparing the actually measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, wherein the actually measured wind speed turbulence curve is divided into two types, namely a group A1 exceeding the IEC standard, and a group A2 not exceeding the IEC standard;
S8, accumulating the number of the scattered points of the group A1, and comparing the number with the total number N, wherein the ratio is used as a quantitative evaluation value of the wind condition complexity of the wind field.
Further, in S1, the acquired historical operation data is required to be not less than one month.
Further, in S2, the data cleansing includes repeated value deletion, missing value supplementation, or/and interrupt data elimination.
In step S3, correcting the wind speed data meeting the requirements according to the ambient temperature and the on-site altitude, and converting the wind speed data into the standard air density, wherein the specific steps are as follows:
3.1, calculating a real-time air density ρ 1 according to the altitude h and the real-time air temperature t:
ρ1＝1.293/(10(/(00×((3+t)/3));
3.2, wind speed conversion is carried out according to density:
Vi Measuring is each data actually recorded in the wind speed channel, vi (i=1, 2, 3..l) is the converted wind speed data.
Further, in S4, an average wind speed of each preset time interval is calculated, and N average wind speeds are obtained, which specifically are:
vm=average (v1+v2+ & gt+vl)/L, then a series of preset time interval averages Vm1, vm2.. VmN;
L is the number of sampling points in a preset time interval, and VL is the wind speed of the L-th sampling.
Further, in S5, the calculation formula of the average turbulence is:
TurN = standard deviation of wind speed/VmN, then Tur1, tur2, turN are obtained.
Further, the calculation formula of the wind speed standard deviation is as follows:
Vi (i=1, 2, 3..l.) is the converted wind speed data.
In S6, the scatter data set is displayed in the graph, and meanwhile, the IEC standard wind speed turbulence curves corresponding to the four wind areas in the IEC standard are displayed in the graph, wherein the horizontal axis is the average wind speed, and the vertical axis is the average turbulence.
Further, in S7, the number of scatter data of the group A1 exceeding the IEC standard is denoted as count (A1), and the number of scatter data of the group A2 not exceeding the IEC standard is denoted as count (A2), then count (A1) +count (A2) =n;
In S8, the number of scattered points of the group A1 is accumulated and compared with the total number N, and the corresponding formula is Kwind =count (A1)/N;
when Kwind is larger than K0, the wind condition of the wind field is judged to be complex, and the fatigue of the large parts of the unit needs to be continuously paid attention to.
The invention also discloses a wind turbine station wind condition distribution extreme turbulence characteristic identification system, which comprises:
the data acquisition module is used for acquiring and storing historical operation data of each machine position unit of the target wind power plant, wherein the historical operation data comprises wind speed;
the data cleaning module is used for cleaning the acquired historical operation data to obtain wind speed data meeting the requirements;
The conversion module is used for correcting the wind speed data meeting the requirements according to the ambient temperature and the on-site altitude, and ensuring that the wind speed data is compared with an IEC design curve on the same standard;
the average wind speed calculation module is used for selecting wind speed data meeting continuity from data meeting requirements, and dividing the wind speed data meeting continuity into N groups according to preset time intervals;
Calculating the average wind speed of each preset time interval to obtain N average wind speeds;
The average turbulence calculation module is used for calculating average turbulence corresponding to the preset time intervals according to the average wind speed of each preset time interval to obtain N average turbulence;
the wind speed turbulence curve generation module is used for sorting N average wind speeds and N average turbulence to obtain a scatter data set of N groups of average wind speeds and average turbulence, and obtaining an actually measured wind speed turbulence curve;
the curve comparison module is used for comparing the actually measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, and dividing the actually measured wind speed turbulence curve into two groups, wherein one group is a group A1 exceeding the IEC standard, and the other group is a group A2 not exceeding the IEC standard;
And the evaluation module is used for accumulating the number of the scattered points of the group A1 and comparing the number with the total number N, wherein the ratio is used as a quantitative evaluation value of the wind condition complexity of the wind field.
Compared with the prior art, the invention has the following beneficial technical effects:
The invention discloses an extremely turbulent flow characteristic identification method of wind condition distribution of a wind turbine station, which comprises the steps of acquiring wind speed data which has the most severe influence on the design of the wind turbine station, preprocessing the wind speed data into data which meets the requirements, correcting the wind speed based on the regulations in IEC design standards, ensuring that the wind speed is compared with the IEC design curve on the same standard, selecting the wind speed data which meets the requirements, wherein the data meets the requirements, because of the problems of data loss and interruption due to transmission and storage, the problems can cause inaccurate calculation, calculating average wind speed and average turbulence, forming a data set, obtaining an actually measured wind speed turbulence curve, comparing the actually measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, extracting wind speed turbulence data which exceeds the IEC standard wind speed turbulence curve, judging whether the external wind condition of the wind turbine station is consistent with the design conditions or not through statistics and display of the wind speed turbulence distribution, analyzing the data in a given time range of each machine position of the whole wind turbine station under the whole working condition (comprising a state starting machine, stopping, running and the like), establishing the wind condition distribution characteristic of the whole wind condition of the wind turbine station, and carrying out visual presentation, and judging the wind condition of different complicated wind conditions of different positions by visual field characteristics.
The method of the invention can quantitatively judge the complex wind condition on site, mainly by comparing with the design wind condition, determining whether the wind condition actually operated by each machine position exceeds the proportion of the design wind condition, and determining whether to implement control intervention according to the proportion, such as a method of reducing the rotating speed, changing the pitch under special working conditions, and the like.
Drawings
FIG. 1 is a flow chart of a wind turbine farm station wind condition distribution extreme turbulence feature identification method of the present invention;
FIG. 2 is a graphical representation of data from a wind speed turbulence curve formed in accordance with the present invention.
Detailed Description
The objects, technical solutions and advantages of the present invention will be more apparent from the following detailed description with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention.
The components illustrated in the figures and described and shown in the embodiments of the invention may be arranged and designed in a wide variety of different configurations, and thus the detailed description of the embodiments of the invention provided in the figures below is not intended to limit the scope of the invention as claimed, but is merely representative of selected ones of the embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention, based on the figures and embodiments of the present invention.
The invention is based on the SCADA big data set, analyzes and visually displays the wind condition characteristics of the unit, compares the wind condition characteristics with IEC design standard wind conditions, realizes quantitative analysis, and simultaneously visually displays the wind condition characteristics, so that design and operation personnel can more clearly know the actual operation conditions and the differences of the design conditions on site.
As shown in FIG. 1, the invention discloses a wind turbine station wind condition distribution extreme turbulence characteristic identification method, which comprises the following steps:
s1, acquiring and storing historical operation data of each machine position unit of a target wind power plant, wherein the historical operation data comprises wind speed;
S2, performing data cleaning on the acquired historical operation data to obtain wind speed data meeting the requirements;
S3, correcting wind speed data meeting requirements according to the ambient temperature and the on-site altitude, and ensuring that the wind speed data is compared with an IEC design curve on the same standard;
S4, selecting wind speed data meeting continuity from data meeting requirements, and dividing the wind speed data meeting continuity into N groups according to preset time intervals;
Calculating the average wind speed of each preset time interval to obtain N average wind speeds;
s5, calculating average turbulence corresponding to the preset time intervals according to the average wind speed of each preset time interval to obtain N average turbulence;
s6, sorting the data of the S4 and the S5 to obtain N groups of scattered point data sets of average wind speed and average turbulence, and obtaining an actually measured wind speed turbulence curve;
S7, comparing the actually measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, wherein the actually measured wind speed turbulence curve is divided into two types, namely a group A1 exceeding the IEC standard, and a group A2 not exceeding the IEC standard;
S8, accumulating the number of the scattered points of the group A1, and comparing the number with the total number N, wherein the ratio is used as a quantitative evaluation value of the wind condition complexity of the wind field.
The features and properties of the present invention are described in further detail below with reference to examples.
As shown in FIG. 1, the wind turbine station wind condition distribution extreme turbulence characteristic identification method mainly comprises four stages of data preparation, data calculation, visual presentation and quantitative analysis.
1. Data preparation
1) The method comprises the steps of collecting and storing historical operation data of each machine position unit of a target wind power plant, wherein the data is required to be no less than one month, and various operation states and external environment wind conditions can be fully experienced to enable the data to be more effective;
2) The returned data are placed on a presence server for storage according to the designated position and the number;
3) And calling the data and performing data cleaning work, including repeated value deletion, missing value supplementation, interrupted data elimination and the like.
2. Data computation
1) Correcting the wind speed according to the ambient temperature and the on-site altitude, converting the wind speed into standard air density, and ensuring that the wind speed is compared with an IEC design curve on the same standard, wherein the specific steps are as follows:
1.1, calculating the real-time air density according to the altitude h (m) and the real-time air temperature t (° C):
ρ1＝1.293/(10(/(00×(+(73+t)/3))
1.2, wind speed conversion is carried out according to density:
Vi Measuring is each data actually recorded in the wind speed channel, vi (i=1, 2, 3..l) is the converted data for the following calculations.
2) Dividing the cleaned data into N groups according to a segment of 10min for SCADA data meeting the continuity;
3) The average wind speed for each 10min was calculated as vm=average (v1+v2+.+vl)/L, yielding a series of 10min averages Vm1, vm2.. VmN. Where L is the number of samples within 10min, e.g. once every 1 second, then l=600;
4) Calculate the average turbulence for 10min, tur1=standard deviation of wind speed/mean VmN, resulting in Tur1, tur2,.. TurN.
The calculation formula of the wind speed standard deviation is as follows:
Vi (i=1, 2, 3..l.) is the converted wind speed data.
3. Visual display
1) N groups (Tur 1, vm 1), (Tur 2, vm 2) calculated as described above were recorded.
2) As shown in fig. 2, all the scattered points described above are shown in the graph (horizontal axis wind speed, vertical axis turbulence), while the wind speed-turbulence curves corresponding to the four types of wind zones in the IEC standard are shown in the graph (horizontal axis wind speed, vertical axis turbulence).
4. Quantitative analysis
1) Marking scattered points beyond the wind speed turbulence curve, namely dividing the scattered points into two groups, namely a group A1 exceeding IEC standard and a group A2 not exceeding IEC design, wherein the group A1 is count (A1) +count (A2) =N
2) The number of scattered points exceeding the design is accumulated and compared with the total number, and the ratio is as follows:
Kwind=count(A1)/N
The K value is used as a quantitative evaluation value of the complexity of wind conditions of the wind farm.
3) The wind field can be used for alarming, when Kwind is larger than K0, the wind condition of the wind field is considered to be complex, the fatigue of large parts of the unit needs to be continuously concerned, and if necessary, the load is reduced by a control means, so that the damage of the large parts is prevented.
The invention also discloses a wind turbine station wind condition distribution extreme turbulence characteristic identification system, which comprises:
the data acquisition module is used for acquiring and storing historical operation data of each machine position unit of the target wind power plant, wherein the historical operation data comprises wind speed;
the data cleaning module is used for cleaning the acquired historical operation data to obtain wind speed data meeting the requirements;
The conversion module is used for correcting the wind speed data meeting the requirements according to the ambient temperature and the on-site altitude, and ensuring that the wind speed data is compared with an IEC design curve on the same standard;
the average wind speed calculation module is used for selecting wind speed data meeting continuity from data meeting requirements, and dividing the wind speed data meeting continuity into N groups according to preset time intervals;
Calculating the average wind speed of each preset time interval to obtain N average wind speeds;
The average turbulence calculation module is used for calculating average turbulence corresponding to the preset time intervals according to the average wind speed of each preset time interval to obtain N average turbulence;
the wind speed turbulence curve generation module is used for sorting N average wind speeds and N average turbulence to obtain a scatter data set of N groups of average wind speeds and average turbulence, and obtaining an actually measured wind speed turbulence curve;
the curve comparison module is used for comparing the actually measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, and dividing the actually measured wind speed turbulence curve into two groups, wherein one group is a group A1 exceeding the IEC standard, and the other group is a group A2 not exceeding the IEC standard;
And the evaluation module is used for accumulating the number of the scattered points of the group A1 and comparing the number with the total number N, wherein the ratio is used as a quantitative evaluation value of the wind condition complexity of the wind field.
The wind turbine station wind condition distribution extreme turbulence characteristic identification method can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The wind turbine farm wind condition distribution extreme turbulence feature identification method of the present invention, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals. The computer storage media may be any available media or data storage device that can be accessed by a computer, including, but not limited to, magnetic storage (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor storage (e.g., ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid State Disk (SSD)), etc.
In an exemplary embodiment, a computer device is also provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the wind turbine park wind condition distribution extreme turbulence feature identification method. The processor may be a central processing unit (CentralProcessingUnit, CPU), but may also be other general purpose processors, digital signal processors (DigitalSignalProcessor, DSP), application specific integrated circuits (ApplicationSpecificIntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-ProgrammableGateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the specific embodiments of the present invention without departing from the spirit and scope of the present invention, and any modifications and equivalents are intended to be included in the scope of the claims of the present invention.
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| CN101634722A (en) * | 2008-07-22 | 2010-01-27 | 西门子公司 | Method and arrangement for the forecast of wind-resources |
| EP3859149A1 (en) * | 2020-02-03 | 2021-08-04 | General Electric Renovables España S.L. | Turbulence intensity estimation |
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| JP7066374B2 (en) * | 2016-11-28 | 2022-05-13 | 西日本技術開発株式会社 | Turbulence evaluation device, turbulence evaluation method and program |
| CN115270065A (en) * | 2022-07-13 | 2022-11-01 | 中国电建集团华东勘测设计研究院有限公司 | Local effective turbulence intensity calculation method for wind turbine generator based on SCADA (supervisory control and data acquisition) operation data of offshore wind farm |
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| CN101634722A (en) * | 2008-07-22 | 2010-01-27 | 西门子公司 | Method and arrangement for the forecast of wind-resources |
| EP3859149A1 (en) * | 2020-02-03 | 2021-08-04 | General Electric Renovables España S.L. | Turbulence intensity estimation |
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