Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF
It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solv...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6215 |
| _version_ | 1797517179290124288 |
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| author | Mingzhu Tang Jiabiao Yi Huawei Wu Zimin Wang |
| author_facet | Mingzhu Tang Jiabiao Yi Huawei Wu Zimin Wang |
| author_sort | Mingzhu Tang |
| collection | DOAJ |
| description | It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set. |
| first_indexed | 2024-03-10T07:13:08Z |
| format | Article |
| id | doaj.art-099a6bf7913b4b78a842947a34dd756a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:08Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-099a6bf7913b4b78a842947a34dd756a2023-11-22T15:13:24ZengMDPI AGSensors1424-82202021-09-012118621510.3390/s21186215Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERFMingzhu Tang0Jiabiao Yi1Huawei Wu2Zimin Wang3School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaHubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, ChinaSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaIt is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.https://www.mdpi.com/1424-8220/21/18/6215wind turbine generator setelectric pitch systemextreme random forestgrey wolf optimizationfault detection |
| spellingShingle | Mingzhu Tang Jiabiao Yi Huawei Wu Zimin Wang Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF Sensors wind turbine generator set electric pitch system extreme random forest grey wolf optimization fault detection |
| title | Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF |
| title_full | Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF |
| title_fullStr | Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF |
| title_full_unstemmed | Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF |
| title_short | Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF |
| title_sort | fault detection of wind turbine electric pitch system based on igwo erf |
| topic | wind turbine generator set electric pitch system extreme random forest grey wolf optimization fault detection |
| url | https://www.mdpi.com/1424-8220/21/18/6215 |
| work_keys_str_mv | AT mingzhutang faultdetectionofwindturbineelectricpitchsystembasedonigwoerf AT jiabiaoyi faultdetectionofwindturbineelectricpitchsystembasedonigwoerf AT huaweiwu faultdetectionofwindturbineelectricpitchsystembasedonigwoerf AT ziminwang faultdetectionofwindturbineelectricpitchsystembasedonigwoerf |