JPH05141206A - Power plant performance diagnosis method - Google Patents

Abstract

(57) [Summary] [Purpose] When diagnosing the performance of plant equipment, the performance abnormality of the equipment to be diagnosed is automatically checked online and also by referring to past time-series efficiency data of equipment of the same type at other power plants. Provide a method to diagnose. [Configuration] Normal efficiency data of the components of each power plant are stored in time series, and when performing performance diagnosis, the past time-series efficiency data of the diagnostic target device and the saved diagnostic target devices of other power plants are saved. Using the past time-series efficiency data of the same type of equipment, the degree of variation between the predicted efficiency at the time of performance diagnosis and the past efficiency data is obtained, and the difference between the actual efficiency at the time of diagnosis and the predicted efficiency is the data data variation degree. If it exceeds the abnormality judgment tolerance threshold value determined from the above, it is judged as a performance abnormality of the diagnosis target device.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION The present invention relates to preventive maintenance of steam turbines, condensers, feed water superheaters, boilers, etc. installed in, for example, thermal power / nuclear power plant, etc. The present invention relates to a performance diagnosis method for a power plant to be diagnosed.

[0002]

2. Description of the Related Art Huge power plant systems such as thermal power plants and nuclear power plants are important assets for their owners, and the economic benefits to the owners are the components of the power plant. It greatly changes depending on the performance during operation. For example, 100
A 1% difference in the thermal efficiency of a steam turbine that drives a 10,000 kilowatt class generator is considered to be worth several billion yen. Therefore, it is extremely important to maintain and operate the plant components so that the operation performance of the components is always optimum.

Conventionally, power generation monitoring and control systems for thermal power plants and the like have performance calculation and management functions thereof, and process state quantities collected by a digital controller under a fixed condition, such as flow rate, temperature, and pressure. Or, based on the output of the generator, calculate the transmission end efficiency, power generation end efficiency, boiler efficiency, feedwater superheater performance, condenser performance, turbine efficiency, etc., and manage the power plant based on these calculation results. An engineer performs performance management and judgment of performance abnormality by periodically checking the long-term change tendency of the efficiency of each device.

[0004]

In order to perform preventive maintenance of each component equipment of a power plant, it is essential to be able to accurately determine the performance abnormality of each component equipment.

However, in the conventional method of performance management / abnormality determination of plant component equipment, the work of determining a performance abnormality mainly depends on a human system, which is very troublesome.
In particular, determining abnormal performance changes requires a long period of time because it requires accumulation of efficiency data and detailed analysis work, and it takes time to perform detailed inspections for operational management and preventive maintenance of each component of the power plant. There were cases where the data could not be fully utilized in a timely manner during the planning of repairs and repairs.

Further, in the performance diagnosis, if the efficiency data of the same type equipment in another power plant is also referred to, the diagnosis accuracy is further improved, but the number of data used becomes enormous and what kind of data is collected. Due to insufficient examination in terms, it was in a situation where it could not be used in practice.

The present invention has been made in view of the above circumstances, and when diagnosing the performance of plant equipment, it refers to the past time series efficiency data of the same type equipment of other power plants automatically and online. Therefore, it is an object of the present invention to provide a performance diagnosis method for a power plant that can accurately perform a performance abnormality of a diagnosis target device.

[0008]

A method for diagnosing a performance of a power plant according to the present invention is a method for diagnosing a performance of a power plant, which performs performance diagnosis of constituent equipment of the plant based on process data during operation of the power plant. The normal efficiency data of the components of each power plant are saved in time series, and when performing performance diagnosis, the same as the saved diagnosis target device of other power plants along with past time series efficiency data of the diagnostic target device. Using the past time-series efficiency data of the model equipment, obtain the degree of variation between the predicted efficiency at the time of performance diagnosis and the past efficiency data.The difference between the actual efficiency at the time of diagnosis and the estimated efficiency is the data data variation degree. It is characterized in that it is judged to be a performance abnormality of the device to be diagnosed when it exceeds the abnormality determination tolerance threshold determined from the above.

[0009]

In the method of the present invention having the above-described structure, at the time of performance diagnosis, first, the past time-series efficiency data of the equipment of the same type as the equipment to be diagnosed at another power plant is searched, and this efficiency data and the past of the equipment to be diagnosed are retrieved. Based on the time-series efficiency data, the prediction efficiency at the time of performance diagnosis and the variation degree of the efficiency data are obtained.

Further, when the difference between the actual efficiency at the time of diagnosing the device to be diagnosed and the predicted efficiency becomes equal to or more than the abnormality determination margin threshold value determined from the variation degree of the efficiency data, it is determined that the performance is abnormal. .. On the other hand, if it is not determined that the performance is abnormal, the actual efficiency at the time of diagnosis is stored in the storage unit to prepare for the next diagnosis.

[0011]

Embodiments of the present invention will now be described with reference to the drawings. FIG. 1 shows an example of the configuration of a power plant performance diagnostic device used in the method of the present invention.

In FIG. 1, the integrated performance diagnosis device 1 is connected to the unit control computer devices 3a, 3b, ... Of the power stations A, B ,. These control computer devices collectively control a control device (not shown) that controls plant components such as steam turbines, condensers, feed water superheaters, and boilers of each power plant, and the data transmission device 4a. , 4b, ... Also,
These data transmission devices 4a, 4b, ... Include a steam turbine performance diagnosis device 5a for performing performance diagnosis of plant constituent devices (not shown) such as steam turbines, condensers, feed water superheaters, and boilers of each power plant. , 5b, ..., Condenser performance diagnostic device 6a, 6b, ..., Water supply superheater performance diagnostic device (not shown), Boiler performance diagnostic device (not shown), etc. are connected. FIG. 2 shows an example of a processing flow in the power plant performance diagnosis method of the present invention. In the figure, a process step 101 collects process values for performance diagnosis, a process step 102 makes a performance diagnosis activation determination, and a process step 103 determines whether or not the activation condition necessary for performance diagnostic activation is satisfied. I do. If the start condition is not satisfied in processing step 103, the process returns to processing step 101, and the process value for performance diagnosis in the next cycle is collected.

On the other hand, when it is determined in the processing step 103 that the starting condition is satisfied, the processing step 104,
The processing of 105, 106 and 107 is performed. That is, in processing step 104, the past time-series efficiency data of the equipment of the same type as the equipment to be performance-diagnosed at another power plant is searched. In processing step 105, the actual efficiency at the time of diagnosis of the performance diagnosis target device is obtained. In processing step 106, the predicted efficiency at the time of diagnosis and the degree of variation in efficiency data are calculated from the past time-series efficiency data of the equipment of the same type as the performance diagnosis target equipment of another power plant and the past time-series efficiency data of the diagnosis target equipment. Ask. continue,
In the processing step 107, when the difference between the actual efficiency at the time of diagnosis and the predicted efficiency is equal to or larger than the abnormality determination margin threshold value determined from the variation degree of the efficiency data in the past, it is determined that the performance is abnormal.

In the present invention, the processing steps 101 to 101 described above are performed.
The processing of 103 is performed by the calculation device 3 for unit control of each power plant.
a, 3b, ..., Process step 104 is performed by the integrated performance diagnostic device 1, and process steps 105 to 10 are performed.
7 is a steam turbine performance performance diagnostic device 5a, 5b, ..., which is an individual performance diagnostic device of each power plant, a condenser performance diagnostic device 6a, 6b, ..., a feed water heater performance diagnostic device (not shown), It is performed by a boiler performance diagnostic device (not shown). Further, although not shown in FIG. 2, when the performance is determined to be normal, a process of storing the actual efficiency at the time of diagnosis in the power plant group efficiency storage unit of the integrated performance diagnostic device is performed.

In this embodiment, except for the integrated performance diagnostic device 1 as a common device, each power plant is constituted by the same means, and therefore in the following description, the power plant A will be referred to as the power plant. Further, the detailed configuration will be described with respect to the steam turbine performance diagnostic device 5a as the performance diagnostic device.

The unit control computer device 3a controls the monitoring and control functions of the power plant, and FIG. 3 shows an example of its internal configuration. In the figure, the data transmission / reception processing means 9 exchanges various data with each individual performance diagnostic device connected thereto via the data transmission device 4a, and also connects to it via the data transmission device 2. Various types of data are exchanged with the integrated performance diagnostic device 1.

The process value collection means 10 for performance diagnosis is a process necessary for performance diagnosis of a steam turbine, a condenser, a feed water heater, a boiler, etc. from a process data group collected by a control device (not shown). The data is collected in a constant cycle T, and the collected process data is stored in the process value storage unit 11. FIG. 4 exemplifies an aspect of the stored process data. In the case of steam turbine performance diagnosis, boiler inlet feed water amount, main steam pressure, reheat steam pressure, condenser vacuum degree, main steam temperature. , Reheat steam temperature, generated power output (load), etc. are stored. The process value storage unit 11 also stores a condenser, a feed water superheater, and boiler performance diagnostic process data at the same time.

The performance diagnosis start determination means 12 changes the settling of the plant as a settling index stored in the process value storage section 11 (generator output) and main steam / reheat steam temperature data with respect to time. Is determined based on whether or not the fluctuation range is within a preset fluctuation range, and the timing at which a performance diagnosis execution command is output to each individual performance diagnosis device is determined from the settling / unsettling information of the plant.
Details of the timing of activating this performance diagnosis process are shown in FIG.
And as shown in FIG. 6, after the load of the power plant reaches a certain level, the load fluctuation width is A minutes, and the preset range ΔW
If the load settling condition is satisfied, then whether the main steam / reheat steam temperature is within the preset range ΔT for B minutes together with the load settling condition is checked. Check the main steam / reheat steam temperature setting.
Next, the load settling judgment and the main steam / reheat steam temperature settling judgment are performed simultaneously and periodically, and the load settling condition / main steam /
When any one of the reheat steam temperature settling conditions shifts from settling to unstabilization, the data transmission / reception processing means 9 sends the data to the integrated performance diagnostic device 1 via the data transmission device 2 to identify the equipment of the same type as the diagnostic equipment of another power plant. It outputs a search execution command for past time-series efficiency data, and the data transmission / reception processing means 9 outputs a performance diagnosis execution command to each individual performance diagnostic device via the data transmission device 4a.

FIG. 7 shows a specific configuration example of the integrated performance diagnostic device 1. In FIG. 1, the data transmission / reception processing means 13 exchanges various data with the unit control computer devices 3 a, 3 b, ... Of each power plant via the data transmission device 2. The power plant group efficiency storage unit 14 stores time-series efficiency data of steam turbines, condensers, feed water superheaters, boilers, etc., which are components of each power plant, together with time data in a manner as shown in FIG. Store. The efficiency data search means 15 sends a search execution command from the performance diagnosis start determination means (for example, 12) in the unit control computer device (for example, 3a) of each power plant to the data transmission / reception processing means 13 via the data transmission device 2. When received by, the power plant group efficiency storage unit 1
From 4, the past time-series efficiency data of the device of the same model as the device to be diagnosed is retrieved. The past time-series efficiency data retrieved in this manner is transmitted by the data transmission / reception processing means 13 to the unit control computer device (for example, 3a) of the corresponding power plant via the data transmission device 2. Next, the data transmission / reception processing means (for example, 9) in the unit control computer device (for example, 3a) uses this time-series efficiency data via the data transmission device (for example, 4a) to diagnose the individual performance of the diagnostic target device. Send to. The efficiency data storage means 16 is transmitted from the individual performance diagnostic device of each power plant, and is a data transmission device (for example, 4a) → a unit control computer device (for example, 3a) → a data transmission device (for example, 2).
The normal efficiency data at the time of performance diagnosis, which is received by the data transmission / reception processing unit 13 via the, is stored in the power plant group efficiency storage unit 14 together with the time data.

Here, when the performance diagnosis execution determining command 12 in the unit control computer 3a outputs a performance diagnosis execution command, the process data necessary for each individual performance diagnosis device is stored in the process value storage shown in FIG. The data is selected from the data stored in the unit 11 and transmitted by the data transmission / reception processing means 9 to each individual performance diagnostic device via the data transmission device 4a.

FIG. 9 shows a specific example of the construction of the steam turbine performance diagnostic device 5a used in the method of the present invention. In the figure, the data transmission / reception processing diagnosis 17 exchanges various data with other devices via the data transmission device 4a. The equipment efficiency calculation means 18 calculates equipment efficiency, which is an index for performance abnormality determination, based on the performance diagnosis process data captured via the data transmission device 4a by the data transmission / reception processing diagnosis 17. Here, the indicators of the performance abnormality determination are turbine efficiency in the case of a steam turbine, cooling pipe cleanliness in the case of a condenser, terminal temperature (TD) and drain outlet temperature difference (DC) in the case of a feed water heater, and boiler. In case of, it is boiler efficiency.

In the case of steam turbine performance diagnosis, the equipment efficiency calculation means 18 calculates the enthalpy and the like from the storage of the process value storage unit 11, that is, from the steam temperature, pressure, etc., and the internal efficiency η of the steam turbine is given by the following equation. Calculate based on η = HU / HA

As shown in the enthalpy-entropy diagram of FIG. 10, HA is the nozzle inlet vapor temperature T1, the adiabatic heat drop from the pressure P1 to the blade outlet P2, and HU is the nozzle inlet vapor temperature T1 and the pressure P1 state. And the enthalpy difference at the blade outlet steam temperature T2 and pressure P2.

The turbine efficiency is obtained from the first paragraph to the nth paragraph of the steam turbine, and the calculation results are shown in FIG.
In the mode as illustrated in FIG. 3, the data is stored in the device efficiency storage unit 19 together with the diagnosis year / month / hour data.

Further, as described above, the efficiency of the equipment of the same type at another power plant required for the performance diagnosis is transmitted by the integrated performance diagnosis device 1 and then transmitted to the "B power generation searched for this performance diagnosis". "Temperature efficiency time series data of power station", "Time series data of turbine efficiency of C power station retrieved for performance diagnosis this time", ... It is stored in the unit 20.

As shown in FIG. 13, the predictive efficiency calculation means 21 stores the data (indicated by the solid black circles in FIG. 13) of the equipment subject to performance diagnosis for the past fixed period (for example, one month) from the time of performance diagnosis. Using the efficiency data of other power stations (shown by the solid white circle in FIG. 13), extrapolation is performed using the method of least squares or the like to obtain the predicted efficiency AOS ′ at the time of performance diagnosis.

The efficiency data variation degree calculation means 22 uses the turbine efficiency data stored in the equipment efficiency storage unit 19 for a certain period in the past from the time of performance diagnosis and the other power generation equipment stored in the other power station equipment efficiency storage unit 20. From the turbine efficiency data for a certain period in the past from the time of performance diagnosis, the degree of efficiency variation is calculated as the standard deviation.

As shown in FIG. 13, the performance abnormality determination means 23 determines the abnormality based on the predicted efficiency AOS 'calculated by the predicted efficiency calculation means 21 and the variation degree calculated by the efficiency data variation degree calculation means 22. Tolerance threshold D
The range defined by AO is determined by whether the difference between the predicted value AOS ′ and the actual efficiency A at the time of diagnosis has exceeded. This abnormality determination margin threshold DAO is calculated as n times the standard deviation σ, as shown in the following equation. | A-AOS '| ≧ DAO DAO = nσ

The abnormality determination result by the performance abnormality determining means 23 is stored in the equipment efficiency storage unit 19 shown in FIG. 11 as flag data (normal: 0, abnormality: 1) together with turbine efficiency. Although not described as an example in the present invention, this flag data is used for alarm output and the like.

Finally, when the performance abnormality determining means 23 determines that the performance is normal, the data transmission / reception processing means 17 determines the diagnosis date / time data and the actual efficiency data stored in the equipment efficiency storage section 19. , Data transmission device 4a → A
It is transmitted to the integrated performance diagnosis device 1 via the power station unit control computer device 3a → data transmission device 2. The efficiency data storage means 16 in the integrated performance diagnostic device 1 stores the transmitted normal efficiency data at the time of diagnosis of the power plant A in the power plant group efficiency storage unit 14.

As described above, in the present invention,
At the time of performance diagnosis, first, the efficiency data retrieval means 15 retrieves past time-series efficiency data of the equipment of the same type as the equipment to be diagnosed from the power plant group efficiency storage unit 14 in the integrated performance diagnosis apparatus 1. At this time, the efficiency data of the series is transmitted to the corresponding power plant by the data transmission / reception processing means 13 in the overall performance diagnosis apparatus 1, and passes through the unit control computer apparatus (for example, 3a) to individually diagnose the equipment to be diagnosed. The data is transmitted to the performance diagnostic device (for example, 5a), and is also stored in the other power station equipment efficiency storage unit 20 by the data transmission / reception processing means 17 in the individual performance diagnostic device.

In the individual performance diagnosing device of each power station, the equipment efficiency calculating means 18 calculates the actual efficiency at the time of performance diagnosis, and the predicted efficiency calculating means 21 stores it in the other power station equipment efficiency storing section 20. Past time-series efficiency data of equipment of the same type at other power plants and equipment efficiency storage unit 1
The predicted efficiency at the time of performance diagnosis is obtained based on the past time-series efficiency data of the diagnosis target device stored in 9. Further, the efficiency data variation degree calculating unit 22 stores the past time-series efficiency data of the same type equipment of another power plant stored in the other power station equipment efficiency storage unit 20, and the diagnosis stored in the equipment efficiency storage unit 19. Based on the past time-series efficiency data of the target device, the degree of variation in the past time-series efficiency data is obtained.

Finally, the performance abnormality determining means 23 calculates the efficiency data variation degree calculating means 22 based on the difference between the actual efficiency at the time of the performance abnormality determining diagnosis of the diagnosis target equipment and the predicted efficiency obtained by the predictive efficiency calculating means 21. When it exceeds the abnormality determination tolerance threshold value determined from the variation degree obtained in, it is determined that the performance of the diagnostic device is abnormal.

On the other hand, after the performance diagnosis, the performance abnormality judging means 23
When it is determined that the performance is normal, the data transmission / reception processing unit 17 transmits normal efficiency data to the overall performance diagnosis apparatus 1 via the unit control computer apparatus. Efficiency data storage means 16 in the integrated performance diagnostic device 1
Stores the transmitted normal efficiency data in the power plant group efficiency storage unit 14 in preparation for the next diagnosis.

[0035]

As described above, according to the present invention, when determining a performance abnormality during operation of a steam turbine, a condenser, a feedwater superheater, a boiler, etc., which are components of a power plant, it is automatically online. In addition to past time-series efficiency data of the diagnosis target device, past time-series efficiency data of the same type of equipment at other power plants are also referred, so it is possible to accurately judge the performance abnormality of the diagnosis target device, and prevent The determination results can be used in a timely and sufficient manner for planning such as detailed inspection and maintenance for maintenance.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing a configuration example of a performance diagnostic device used in a method of the present invention.

FIG. 2 is a flowchart illustrating a performance diagnosis processing flow according to the method of the present invention.

FIG. 3 is an explanatory diagram showing a specific configuration example of the unit control computer shown in FIG. 1.

FIG. 4 is an explanatory diagram illustrating a mode of process data stored in a process value storage unit illustrated in FIG.

FIG. 5 is a graph for explaining the timing of starting the performance diagnosis processing in the method of the present invention.

FIG. 6 is a flowchart for explaining a performance diagnosis activation determination operation in the method of the present invention.

FIG. 7 is an explanatory diagram showing a specific configuration example of the overall performance diagnostic device shown in FIG. 1.

FIG. 8 is an explanatory diagram illustrating an example of various data stored in a power plant group efficiency storage unit illustrated in FIG. 7.

FIG. 9 is an explanatory diagram showing a specific configuration example of the steam turbine performance diagnostic device shown in FIG. 1.

FIG. 10 is an explanatory diagram of calculation of turbine efficiency.

FIG. 11 is an explanatory diagram illustrating a mode of calculation result data stored in the device efficiency storage unit illustrated in FIG. 9.

12 is an explanatory diagram illustrating an example of various data stored in another power station device efficiency storage unit illustrated in FIG. 9. FIG.

FIG. 13 is a graph illustrating the performance abnormality determination according to the present invention.

[Explanation of symbols]

1 ... Overall performance diagnostic device, 2 ... Data transmission device, 3a, 3
b ... Unit control computer device, 4a, 4b ... Data transmission device, 5a, 5b ... Steam turbine performance diagnostic device, 6
a, 6b ... Condenser performance diagnostic device, 9 ... Data transmission / reception processing means, 10 ... Performance diagnostic process value collection means, 11 ... Process value storage section, 12 ... Performance diagnostic start determination means, 13 ...
Data transmission / reception processing means, 14 ... Power plant group efficiency storage section, 1
5 ... efficiency data search means, 16 ... efficiency data storage means,
17 ... Data transmission / reception processing means, 18 ... Equipment efficiency calculation means, 19 ... Equipment efficiency storage section, 20 ... Other power station equipment efficiency storage section, 21 ... Predicted efficiency calculation means, 22 ... Efficiency data variation degree calculation means, 23 ... Performance Abnormality judgment means.

Claims (1)

[Claims] 1. A method for diagnosing the performance of a component device of a power plant based on process data during operation of the power plant, wherein a normal efficiency data of the component device of each power plant is chronologically arranged. When saving and performing a performance diagnosis, together with past time-series efficiency data of the diagnosis target device, using the past time-series efficiency data of the same type device as the saved diagnosis target device of the other power plant,
When the difference between the predicted efficiency at the time of performance diagnosis and the past efficiency data is obtained, and the difference between the actual efficiency at the time of performance abnormality diagnosis and the predicted efficiency is equal to or greater than the abnormality determination margin threshold value determined from the above-mentioned variation degree. A method for diagnosing a performance of a power plant, comprising: