Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA
The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of posi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6741 |
| _version_ | 1797586060749832192 |
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| author | Lingchao Kong Hongtao Liang Guozhu Liu Shuo Liu |
| author_facet | Lingchao Kong Hongtao Liang Guozhu Liu Shuo Liu |
| author_sort | Lingchao Kong |
| collection | DOAJ |
| description | The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate. |
| first_indexed | 2024-03-11T00:17:56Z |
| format | Article |
| id | doaj.art-f162a9c997664418879f6da026782b28 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:56Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f162a9c997664418879f6da026782b282023-11-18T23:33:51ZengMDPI AGSensors1424-82202023-07-012315674110.3390/s23156741Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSALingchao Kong0Hongtao Liang1Guozhu Liu2Shuo Liu3School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaSchool of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, ChinaThe internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.https://www.mdpi.com/1424-8220/23/15/6741wind turbineCatBoost algorithmfault detectioncategory imbalanceintelligent optimization algorithm |
| spellingShingle | Lingchao Kong Hongtao Liang Guozhu Liu Shuo Liu Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA Sensors wind turbine CatBoost algorithm fault detection category imbalance intelligent optimization algorithm |
| title | Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA |
| title_full | Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA |
| title_fullStr | Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA |
| title_full_unstemmed | Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA |
| title_short | Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA |
| title_sort | research on wind turbine fault detection based on the fusion of asl catboost and ttrsa |
| topic | wind turbine CatBoost algorithm fault detection category imbalance intelligent optimization algorithm |
| url | https://www.mdpi.com/1424-8220/23/15/6741 |
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