Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting
In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/9/3258 |
| _version_ | 1797568646021644288 |
|---|---|
| author | Zidong Wu Xiaoli Wang Baochen Jiang |
| author_facet | Zidong Wu Xiaoli Wang Baochen Jiang |
| author_sort | Zidong Wu |
| collection | DOAJ |
| description | In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms. |
| first_indexed | 2024-03-10T19:59:55Z |
| format | Article |
| id | doaj.art-f1b3393b25c240c3adfbb59bfd09808e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:59:55Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f1b3393b25c240c3adfbb59bfd09808e2023-11-19T23:43:05ZengMDPI AGApplied Sciences2076-34172020-05-01109325810.3390/app10093258Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient BoostingZidong Wu0Xiaoli Wang1Baochen Jiang2School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, ChinaIn order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.https://www.mdpi.com/2076-3417/10/9/3258wind turbinesfault diagnosisdata miningReliefFeXtreme Gradient Boosting (XGBoost)Supervisory Control And Data Acquisition (SCADA) |
| spellingShingle | Zidong Wu Xiaoli Wang Baochen Jiang Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting Applied Sciences wind turbines fault diagnosis data mining ReliefF eXtreme Gradient Boosting (XGBoost) Supervisory Control And Data Acquisition (SCADA) |
| title | Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting |
| title_full | Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting |
| title_fullStr | Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting |
| title_full_unstemmed | Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting |
| title_short | Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting |
| title_sort | fault diagnosis for wind turbines based on relieff and extreme gradient boosting |
| topic | wind turbines fault diagnosis data mining ReliefF eXtreme Gradient Boosting (XGBoost) Supervisory Control And Data Acquisition (SCADA) |
| url | https://www.mdpi.com/2076-3417/10/9/3258 |
| work_keys_str_mv | AT zidongwu faultdiagnosisforwindturbinesbasedonrelieffandextremegradientboosting AT xiaoliwang faultdiagnosisforwindturbinesbasedonrelieffandextremegradientboosting AT baochenjiang faultdiagnosisforwindturbinesbasedonrelieffandextremegradientboosting |