CN116956047B - Wind turbine generator system performance evaluation system based on wind power generation data - Google Patents
Wind turbine generator system performance evaluation system based on wind power generation data Download PDFInfo
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Abstract
The application relates to the technical field of wind turbine performance evaluation, in particular to a wind turbine performance evaluation system based on wind power generation data, which comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data visualization module, a data processing module, a performance evaluation module and a secondary evaluation module; the data acquisition module acquires environmental factor monitoring index data affecting the performance of the distributed wind power equipment in a target area; the data visualization module builds a digital twin model of the wind turbine generator; the data processing module constructs a fitting curve for representing the electric energy value of the unit wind speed; the performance evaluation module evaluates the performance of the distributed wind power equipment based on the fitting curve; the secondary evaluation module is used for constructing a performance loss model to compensate the unit wind speed and the power generation amount of the distributed wind power equipment, and performing secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed and the power generation amount, so that the accuracy of the performance evaluation of the wind turbine generator is obviously improved.
Description
Technical Field
The application relates to the technical field of wind turbine performance evaluation, in particular to a wind turbine performance evaluation system based on wind power generation data.
Background
At present, a plurality of running wind power plants exist, the actual generated energy of the wind power plants is greatly different from the designed generated energy of the previous wind resource evaluation, the extreme condition that the actual generated energy of each wind power plant is 30% -50% lower than the designed value occurs, and certain economic loss is caused for investors, so that the wind resource evaluation method and technology still need to be continuously improved and perfected.
The performance evaluation method of the wind turbine generator commonly used in the industry at present is rough, the value range is wide, large errors are easy to occur due to lack of refinement and quantification, and in the related technology, when the wind turbine generator is subjected to energy efficiency fault diagnosis, the expert in the field generally performs manual energy efficiency diagnosis according to expert knowledge, personal experience and the like.
However, in practical application, because the wind turbine generator set contains a lot of operation data, the working condition environment where the wind turbine generator set is located is complex, and the like, a plurality of factors influencing the energy efficiency of the wind turbine generator set are arranged. In the above diagnosis mode of only performing manual energy efficiency diagnosis by an expert, the problems of deviation of diagnosis results, low reliability of the diagnosis results, complicated diagnosis process, low efficiency and the like may occur, and in order to solve the above technical problems, a wind turbine generator performance evaluation system based on wind power generation data is provided.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide a wind turbine generator performance evaluation system based on wind power generation data, which comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data visualization module, a data processing module, a performance evaluation module and a secondary evaluation module;
the data acquisition module is used for acquiring environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area;
the data visualization module is used for constructing a digital twin model of the wind turbine, which displays wind power generation data and environmental factor monitoring index data of the distributed wind power equipment in real time;
the data processing module is used for constructing a fitting curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time;
the performance evaluation module is used for performing performance evaluation on the distributed wind power equipment based on the fitting curve to obtain a performance evaluation result, wherein the performance evaluation result comprises normal performance and undetermined performance;
the secondary evaluation module is used for constructing a performance loss model, compensating the unit wind speed and the generated energy of the distributed wind power equipment with undetermined performance based on the performance loss model, and performing secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed and the generated energy.
Further, the process of acquiring the environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area by the data acquisition module comprises the following steps:
acquiring the geographic position of the distributed wind power equipment in the target area, acquiring GIS geographic data within a preset geographic position range of the distributed wind power equipment by a GIS means, and acquiring the topographic features of the geographic position according to the GIS geographic data;
acquiring an external environment source according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and extracting environmental factors related to the external environment source;
acquiring historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using a big data method, carrying out statistical analysis on the historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using the environmental factors, acquiring burst abnormal times corresponding to the environmental factors, and screening the environmental factors according to the burst abnormal times corresponding to the environmental factors;
summarizing the screened environmental factors to obtain environmental factors affecting the performance of the distributed wind power equipment, and obtaining corresponding environmental factor monitoring indexes according to the environmental factors;
and determining the corresponding sensor type through the environmental factor monitoring index, determining the layout range of each type of sensor according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and determining the layout quantity of each type of sensor according to the burst abnormal times corresponding to each environmental factor in the layout range.
Further, the process of acquiring the burst abnormal times corresponding to each environmental factor by the data acquisition module includes:
acquiring index data corresponding to each environmental factor related to an external environmental source, marking acquisition time, setting an acquisition period, acquiring average index data corresponding to each environmental factor related to the external environmental source when the performance of the distributed wind power equipment is normal, and setting an index data threshold value and an accumulated time threshold value;
and acquiring the accumulated time of the difference absolute value between the index data of the environmental factors related to the external environmental source in the acquisition period and the corresponding average index data is larger than an index data threshold, and marking the environmental factors in the acquisition period as burst abnormality when the accumulated time is larger than the accumulated time threshold.
Further, the process of constructing the digital twin model of the wind turbine generator for displaying the wind power generation data and the environmental factor monitoring index data of the distributed wind power equipment in real time by the data visualization module comprises the following steps:
acquiring an entity size diagram of the distributed wind power equipment in the target area, carrying out three-dimensional modeling on the distributed wind power equipment according to the entity size diagram, and matching a three-dimensional model of the distributed wind power equipment in the target area with the geographic position of the corresponding distributed wind power equipment to construct a digital twin basic virtual scene model;
the method comprises the steps of obtaining wind power generation data and environment factor monitoring index data of distributed wind power equipment at each moment in a target area, preprocessing the wind power generation data and the environment factor monitoring index data in a data format to generate real-time twin data, mapping the real-time twin data generated by the distributed wind power equipment to a three-dimensional model of corresponding distributed wind power equipment in a digital twin basic virtual scene model, and generating a digital twin model of a wind turbine.
Further, the process of constructing a fitting curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time by the data processing module comprises the following steps:
collecting the current time power generation power and the surrounding real-time wind speed of the distributed wind power equipment, and calculating the ratio of the current time power generation power to the real-time wind speed to obtain a unit wind speed electric energy value;
and constructing a fitting curve with the ordinate as the unit wind speed electric energy value and the abscissa as time aiming at the unit wind speed electric energy value.
Further, the performance evaluation module performs performance evaluation on the distributed wind power equipment based on the fitting curve, and the process of obtaining the performance evaluation result comprises the following steps:
acquiring the evaluation time required for evaluating the performance of the distributed wind power equipment, and acquiring the average unit wind speed electric energy value and the average unit wind speed electric energy generation amount of the distributed wind power equipment with the same evaluation time in a plurality of historical acquisition periods taking the year as the acquisition period length by using a big data method;
setting the length of an evaluation period for evaluating the performance of the wind turbine generator as a day, and comparing the unit wind speed electric energy value in the fitting curve in the evaluation period with the average unit wind speed electric energy value;
if the unit wind speed electric energy value is smaller than the average unit wind speed electric energy value, intercepting a corresponding curve segment in a corresponding fitting curve, marking the curve segment as red, marking the curve segment as a low-performance segment, and integrating time of all the low-performance segments in an evaluation period to obtain unit wind speed electric energy generation amount;
setting a deviation threshold value, comparing the unit wind speed generating capacity with the average unit wind speed generating capacity, and marking the distributed wind power equipment as normal in performance when the deviation value of the unit wind speed generating capacity and the average unit wind speed generating capacity is smaller than or equal to the deviation threshold value;
when the deviation value of the unit wind speed generating capacity and the average unit wind speed generating capacity is larger than the deviation threshold value, marking the distributed wind power equipment as the undetermined performance, and compensating the unit wind speed generating capacity of the distributed wind power equipment.
Further, the process of constructing the performance loss model by the secondary evaluation module comprises the following steps:
and constructing a performance loss model based on deep learning, classifying the unit wind speed generating capacity of the distributed wind power equipment according to environmental factor monitoring index data, acquiring the average unit wind speed generating capacity of the distributed wind power equipment under different environmental factor monitoring index data, acquiring the deviation value of the unit wind speed generating capacity of the distributed wind power equipment and the average unit wind speed generating capacity under different environmental factor monitoring index data, taking the deviation value of the unit wind speed generating capacity of the distributed wind power equipment and the average unit wind speed generating capacity under different environmental factor monitoring index data as a training set and a testing set, performing real-time learning training on the performance loss model, inputting the training set into the performance loss model for training until a loss function is stable, storing model parameters, testing the performance loss model through the testing set until the performance loss model meets preset requirements, and outputting the performance loss model.
Further, the process of performing the compensation of the unit wind speed and the generating capacity of the distributed wind power equipment with undetermined performance by the secondary evaluation module based on the performance loss model and performing the secondary evaluation of the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed and the generating capacity comprises the following steps:
inputting environmental factor monitoring index data of the distributed wind power equipment with undetermined performance into a performance loss model, obtaining a unit wind speed and generating capacity compensation value of the distributed wind power equipment, obtaining a compensated unit wind speed and generating capacity of the distributed wind power equipment with undetermined performance according to the unit wind speed and generating capacity compensation value, obtaining an average unit wind speed and generating capacity of the distributed wind power equipment with the same evaluation time in a plurality of historical acquisition periods taking the year as the acquisition period length, and comparing the compensated unit wind speed and generating capacity of the distributed wind power equipment with the average unit wind speed and generating capacity;
when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is smaller than or equal to a deviation threshold value, marking the distributed wind power equipment with undetermined performance as normal performance;
and when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is larger than the deviation threshold value, marking the distributed wind power equipment with undetermined performance as abnormal performance.
Compared with the prior art, the application has the beneficial effects that: firstly, environmental factor monitoring index data affecting the performance of distributed wind power equipment in a target area are obtained, a wind power generation set digital twin model displaying wind power generation data and the environmental factor monitoring index data of the distributed wind power equipment in real time is constructed, when the performance evaluation of the wind power generation set is not in accordance with the requirements, the environmental factor monitoring index data of the wind power equipment is obtained in real time through the wind power generation set digital twin model, the unit wind speed electric energy value of the wind power equipment is compensated through constructing a performance loss model, then the compensated unit wind speed electric energy value is subjected to secondary evaluation, the influence of external environmental factors on the performance evaluation of the wind power generation set is ordered, and the accuracy of the performance evaluation of the wind power generation set is remarkably improved.
Drawings
FIG. 1 is a schematic diagram of a wind turbine performance evaluation system based on wind turbine data according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, a wind turbine generator performance evaluation system based on wind power generation data comprises a monitoring center, wherein the monitoring center is in communication connection with a data acquisition module, a data visualization module, a data processing module, a performance evaluation module and a secondary evaluation module;
the data acquisition module is used for acquiring environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area;
the data visualization module is used for constructing a digital twin model of the wind turbine, which displays wind power generation data and environmental factor monitoring index data of the distributed wind power equipment in real time;
the data processing module is used for constructing a fitting curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time;
the performance evaluation module is used for performing performance evaluation on the distributed wind power equipment based on the fitting curve to obtain a performance evaluation result, wherein the performance evaluation result comprises normal performance and undetermined performance;
the secondary evaluation module is used for constructing a performance loss model, compensating the unit wind speed and the generated energy of the distributed wind power equipment with undetermined performance based on the performance loss model, and performing secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed and the generated energy.
It should be further noted that, in the implementation process, the process of acquiring the environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area by the data acquisition module includes:
acquiring the geographic position of the distributed wind power equipment in the target area, acquiring GIS geographic data within a preset geographic position range of the distributed wind power equipment by a GIS means, and acquiring the topographic features of the geographic position according to the GIS geographic data;
acquiring an external environment source according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and extracting environmental factors related to the external environment source;
acquiring historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using a big data method, carrying out statistical analysis on the historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using the environmental factors, acquiring burst abnormal times corresponding to the environmental factors, and screening the environmental factors according to the burst abnormal times corresponding to the environmental factors;
summarizing the screened environmental factors to obtain environmental factors affecting the performance of the distributed wind power equipment, and obtaining corresponding environmental factor monitoring indexes according to the environmental factors;
and determining the corresponding sensor type through the environmental factor monitoring index, determining the layout range of each type of sensor according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and determining the layout quantity of each type of sensor according to the burst abnormal times corresponding to each environmental factor in the layout range.
It should be further noted that, in the implementation process, the extracting the environmental factors related to the external environmental source includes: air temperature, humidity and precipitation; the high temperature and humidity may affect the cooling effect of the unit, resulting in a decrease in power generation efficiency; precipitation can cause equipment damage or corruption, further influences generating efficiency, and the blade icing phenomenon easily takes place for the high and cold high humidity mountain area wind power plant in winter, greatly reduced generating efficiency.
It should be further noted that, in the implementation process, the process of acquiring the number of burst anomalies corresponding to each environmental factor by the data acquisition module includes:
acquiring index data corresponding to each environmental factor related to an external environmental source, marking acquisition time, setting an acquisition period, acquiring average index data corresponding to each environmental factor related to the external environmental source when the performance of the distributed wind power equipment is normal, and setting an index data threshold value and an accumulated time threshold value;
and acquiring the accumulated time of the difference absolute value between the index data of the environmental factors related to the external environmental source in the acquisition period and the corresponding average index data is larger than an index data threshold, and marking the environmental factors in the acquisition period as burst abnormality when the accumulated time is larger than the accumulated time threshold.
It should be further described that, in the specific implementation process, the process of constructing the wind turbine digital twin model for displaying the wind power generation data and the environmental factor monitoring index data of the distributed wind power equipment in real time by the data visualization module includes:
acquiring an entity size diagram of the distributed wind power equipment in the target area, carrying out three-dimensional modeling on the distributed wind power equipment according to the entity size diagram, and matching a three-dimensional model of the distributed wind power equipment in the target area with the geographic position of the corresponding distributed wind power equipment to construct a digital twin basic virtual scene model;
the method comprises the steps of obtaining wind power generation data and environment factor monitoring index data of distributed wind power equipment at each moment in a target area, preprocessing the wind power generation data and the environment factor monitoring index data in a data format to generate real-time twin data, mapping the real-time twin data generated by the distributed wind power equipment to a three-dimensional model of corresponding distributed wind power equipment in a digital twin basic virtual scene model, and generating a digital twin model of a wind turbine.
It should be further noted that, in the implementation process, the process of constructing the fitted curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time by the data processing module includes:
collecting the current time power generation power and the surrounding real-time wind speed of the distributed wind power equipment, and calculating the ratio of the current time power generation power to the real-time wind speed to obtain a unit wind speed electric energy value;
and constructing a fitting curve with the ordinate as the unit wind speed electric energy value and the abscissa as time aiming at the unit wind speed electric energy value.
It should be further noted that, in the implementation process, the performance evaluation module performs performance evaluation on the distributed wind power equipment based on the fitted curve, and the process of obtaining the performance evaluation result includes:
acquiring the evaluation time required for evaluating the performance of the distributed wind power equipment, and acquiring the average unit wind speed electric energy value and the average unit wind speed electric energy generation amount of the distributed wind power equipment with the same evaluation time in a plurality of historical acquisition periods taking the year as the acquisition period length by using a big data method;
setting the length of an evaluation period for evaluating the performance of the wind turbine generator as a day, and comparing the unit wind speed electric energy value in the fitting curve in the evaluation period with the average unit wind speed electric energy value;
if the unit wind speed electric energy value is smaller than the average unit wind speed electric energy value, intercepting a corresponding curve segment in a corresponding fitting curve, marking the curve segment as red, marking the curve segment as a low-performance segment, and integrating time of all the low-performance segments in an evaluation period to obtain unit wind speed electric energy generation amount;
setting a deviation threshold value, comparing the unit wind speed generating capacity with the average unit wind speed generating capacity, and marking the distributed wind power equipment as normal in performance when the deviation value of the unit wind speed generating capacity and the average unit wind speed generating capacity is smaller than or equal to the deviation threshold value;
when the deviation value of the unit wind speed generating capacity and the average unit wind speed generating capacity is larger than the deviation threshold value, marking the distributed wind power equipment as the undetermined performance, and compensating the unit wind speed generating capacity of the distributed wind power equipment.
It should be further noted that, in the implementation process, the process of constructing the performance loss model by the secondary evaluation module includes:
and constructing a performance loss model based on deep learning, classifying the unit wind speed generating capacity of the distributed wind power equipment according to environmental factor monitoring index data, acquiring the average unit wind speed generating capacity of the distributed wind power equipment under different environmental factor monitoring index data, acquiring the deviation value of the unit wind speed generating capacity of the distributed wind power equipment and the average unit wind speed generating capacity under different environmental factor monitoring index data, taking the deviation value of the unit wind speed generating capacity of the distributed wind power equipment and the average unit wind speed generating capacity under different environmental factor monitoring index data as a training set and a testing set, performing real-time learning training on the performance loss model, inputting the training set into the performance loss model for training until a loss function is stable, storing model parameters, testing the performance loss model through the testing set until the performance loss model meets preset requirements, and outputting the performance loss model.
It should be further noted that, in the specific implementation process, the process that the secondary evaluation module compensates the unit wind speed generating capacity of the distributed wind power equipment with undetermined performance based on the performance loss model, and performs the secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed generating capacity includes:
inputting environmental factor monitoring index data of the distributed wind power equipment with undetermined performance into a performance loss model, obtaining a unit wind speed and generating capacity compensation value of the distributed wind power equipment, obtaining a compensated unit wind speed and generating capacity of the distributed wind power equipment with undetermined performance according to the unit wind speed and generating capacity compensation value, obtaining an average unit wind speed and generating capacity of the distributed wind power equipment with the same evaluation time in a plurality of historical acquisition periods taking the year as the acquisition period length, and comparing the compensated unit wind speed and generating capacity of the distributed wind power equipment with the average unit wind speed and generating capacity;
when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is smaller than or equal to a deviation threshold value, marking the distributed wind power equipment with undetermined performance as normal performance;
and when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is larger than the deviation threshold value, marking the distributed wind power equipment with undetermined performance as abnormal performance.
The above embodiments are only for illustrating the technical method of the present application and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present application may be modified or substituted without departing from the spirit and scope of the technical method of the present application.
Claims (1)
1. The wind turbine generator performance evaluation system based on wind power generation data comprises a monitoring center, and is characterized in that the monitoring center is in communication connection with a data acquisition module, a data visualization module, a data processing module, a performance evaluation module and a secondary evaluation module;
the data acquisition module is used for acquiring environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area;
the process for acquiring the environmental factor monitoring index data affecting the performance of the distributed wind power equipment in the target area by the data acquisition module comprises the following steps:
acquiring the geographic position of the distributed wind power equipment in the target area, acquiring GIS geographic data within a preset geographic position range of the distributed wind power equipment by a GIS means, and acquiring the topographic features of the geographic position according to the GIS geographic data;
acquiring an external environment source according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and extracting environmental factors related to the external environment source;
acquiring historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using a big data method, carrying out statistical analysis on the historical data of the distributed wind power equipment when the performance of the distributed wind power equipment is abnormal by using the environmental factors, acquiring burst abnormal times corresponding to the environmental factors, and screening the environmental factors according to the burst abnormal times corresponding to the environmental factors;
summarizing the screened environmental factors to obtain environmental factors affecting the performance of the distributed wind power equipment, and obtaining corresponding environmental factor monitoring indexes according to the environmental factors;
determining the corresponding sensor type through the environmental factor monitoring index, determining the layout range of each type of sensor according to the geographic position of the distributed wind power equipment and the topographic features of the geographic position, and determining the layout quantity of each type of sensor according to the burst abnormal times corresponding to each environmental factor in the layout range;
the process of acquiring the burst abnormal times corresponding to each environmental factor by the data acquisition module comprises the following steps:
acquiring index data corresponding to each environmental factor related to an external environmental source, marking acquisition time, setting an acquisition period, acquiring average index data corresponding to each environmental factor related to the external environmental source when the performance of the distributed wind power equipment is normal, and setting an index data threshold value and an accumulated time threshold value;
acquiring the accumulated time of the difference absolute value between the index data of the environmental factors related to the external environmental source in the acquisition period and the corresponding average index data is larger than an index data threshold, and marking the environmental factors in the acquisition period as burst abnormality when the accumulated time is larger than the accumulated time threshold;
the data visualization module is used for constructing a digital twin model of the wind turbine, which displays wind power generation data and environmental factor monitoring index data of the distributed wind power equipment in real time;
the process for constructing the digital twin model of the wind turbine generator for displaying the wind power generation data and the environmental factor monitoring index data of the distributed wind power equipment in real time by the data visualization module comprises the following steps:
acquiring an entity size diagram of the distributed wind power equipment in the target area, carrying out three-dimensional modeling on the distributed wind power equipment according to the entity size diagram, and matching a three-dimensional model of the distributed wind power equipment in the target area with the geographic position of the corresponding distributed wind power equipment to construct a digital twin basic virtual scene model;
acquiring wind power generation data and environment factor monitoring index data of distributed wind power equipment at each moment in a target area, preprocessing the wind power generation data and the environment factor monitoring index data in a data format to generate real-time twin data, and mapping the real-time twin data generated by the distributed wind power equipment to a three-dimensional model of the corresponding distributed wind power equipment in a digital twin basic virtual scene model to generate a digital twin model of a wind turbine;
the data processing module is used for constructing a fitting curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time;
the process for constructing the fitting curve representing the change of the electric energy value of the unit wind speed of the distributed wind power equipment along with time by the data processing module comprises the following steps:
collecting the current time power generation power and the surrounding real-time wind speed of the distributed wind power equipment, and calculating the ratio of the current time power generation power to the real-time wind speed to obtain a unit wind speed electric energy value;
aiming at the unit wind speed electric energy value, constructing a fitting curve with an ordinate as the unit wind speed electric energy value and an abscissa as time;
the performance evaluation module is used for performing performance evaluation on the distributed wind power equipment based on the fitting curve to obtain a performance evaluation result, wherein the performance evaluation result comprises normal performance and undetermined performance;
the process of constructing the performance loss model by the secondary evaluation module comprises the following steps:
establishing a performance loss model based on deep learning, classifying unit wind speed generated energy of distributed wind power equipment according to environmental factor monitoring index data, obtaining average unit wind speed generated energy of the distributed wind power equipment under different environmental factor monitoring index data, obtaining deviation values of the unit wind speed generated energy of the distributed wind power equipment and the average unit wind speed generated energy under different environmental factor monitoring index data, taking the deviation values of the unit wind speed generated energy of the distributed wind power equipment and the average unit wind speed generated energy under different environmental factor monitoring index data as a training set and a testing set, performing real-time learning training on the performance loss model, inputting the training set into the performance loss model for training until a loss function is stable, storing model parameters, testing the performance loss model through the testing set until the performance loss model meets preset requirements, and outputting the performance loss model;
the secondary evaluation module is used for constructing a performance loss model, compensating the unit wind speed and the generated energy of the distributed wind power equipment with undetermined performance based on the performance loss model, and performing secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed and the generated energy;
the secondary evaluation module compensates the unit wind speed generated energy of the distributed wind power equipment with undetermined performance based on the performance loss model, and the process of performing secondary evaluation on the performance of the distributed wind power equipment with undetermined performance according to the compensated unit wind speed generated energy comprises the following steps:
inputting environmental factor monitoring index data of the distributed wind power equipment with undetermined performance into a performance loss model, obtaining a unit wind speed and generating capacity compensation value of the distributed wind power equipment, obtaining a compensated unit wind speed and generating capacity of the distributed wind power equipment with undetermined performance according to the unit wind speed and generating capacity compensation value, obtaining an average unit wind speed and generating capacity of the distributed wind power equipment with the same evaluation time in a plurality of historical acquisition periods taking the year as the acquisition period length, and comparing the compensated unit wind speed and generating capacity of the distributed wind power equipment with the average unit wind speed and generating capacity;
when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is smaller than or equal to a deviation threshold value, marking the distributed wind power equipment with undetermined performance as normal performance;
and when the deviation value of the compensated unit wind speed generating capacity and the average unit wind speed generating capacity is larger than the deviation threshold value, marking the distributed wind power equipment with undetermined performance as abnormal performance.
Priority Applications (1)
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| CN104915747A (en) * | 2015-02-03 | 2015-09-16 | 远景能源（江苏）有限公司 | Electricity generation performance evaluation method of generator set and equipment thereof |
| CN114117887A (en) * | 2021-10-28 | 2022-03-01 | 华能利津风力发电有限公司 | Real-time assessment method, system and medium for online power generation performance of wind turbine generator |
| CN116610747A (en) * | 2023-07-21 | 2023-08-18 | 北京国遥新天地信息技术股份有限公司 | Visual intelligent management system based on three-dimensional numbers |
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| CN104915747A (en) * | 2015-02-03 | 2015-09-16 | 远景能源（江苏）有限公司 | Electricity generation performance evaluation method of generator set and equipment thereof |
| CN114117887A (en) * | 2021-10-28 | 2022-03-01 | 华能利津风力发电有限公司 | Real-time assessment method, system and medium for online power generation performance of wind turbine generator |
| CN116610747A (en) * | 2023-07-21 | 2023-08-18 | 北京国遥新天地信息技术股份有限公司 | Visual intelligent management system based on three-dimensional numbers |
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