FAULT DETECTION OF WIND TURBINE PITCH CONNECTION BOLTS BASED ON TSDAS-SMOTE WITH XGBOOST

Mingzhu Tang, Caihua Meng (), Lang Li, Huawei Wu (), Yang Wang, Junbin He (), Yujie Huang (), Yu Yu (), Madini O. Alassafi, Fawaz E. Alsaadi, Adil M. Ahmad and Fuqiang Xiong
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Mingzhu Tang: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Caihua Meng: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Lang Li: ��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China
Huawei Wu: ��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China
Yang Wang: ��School of Electric Engineering, Shanghai Dian ji University, Shanghai 201306, P. R. China§State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, P. R. China
Junbin He: �Hunan Intellectual Property Protection Center, Changsha 410006, P. R. China
Yujie Huang: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Yu Yu: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Madini O. Alassafi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Fawaz E. Alsaadi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Adil M. Ahmad: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Fuqiang Xiong: *State Grid Hunan Extra High Voltage Substation Company, Changsha 410029, P. R. China††Substation Intelligent Operation and Inspection Laboratory, State Grid Hunan Electric Power Co., Ltd., Changsha 410029, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-21

Abstract: For the problem of class-imbalance in the operation monitoring data of wind turbine (WT) pitch connecting bolts, an improved Borderline-SMOTE oversampling method based on “two-step decision†with adaptive selection of synthetic instances (TSDAS-SMOTE) is proposed. Then, TSDAS-SMOTE is combined with XGBoost to construct a WT pitch connection bolt fault detection model. TSDAS-SMOTE generates new samples by “two-step decision making†to avoid the problem of class–class boundary blurring that Borderline-SMOTE tends to cause when oversampling. First, the nearest neighbor sample characteristics are perceived by the fault class samples in the first decision step. If the characteristics of this fault class sample are different from the characteristics of all its nearest neighbor samples, the fault class sample is identified as interference and filtered. Second, the faulty class samples in the boundary zone are extracted as synthetic instances to generate new samples adaptively. Finally, the normal class samples in the boundary zone are used to perceive the unqualified new generated samples in the boundary zone based on the minimum Euclidean distance characteristics, and these unqualified samples are eliminated. For the second step of decision making, since the first step decision removes some of the newly generated samples, the remaining fault class samples without interference samples and boundary zone samples are used as synthetic instances to continue adaptively generating new samples. Thus, a balanced data set with clear class–class boundary zone is obtained, which is then used to train a WT pitch connection bolt fault detection model based on the XGBoost algorithm. The experimental results show that compared with six popular oversampling methods such as Borderline-SMOTE, Cluster-SMOTE, K-means-SMOTE, etc., the fault detection model constructed by the proposed oversampling method is better than the compared fault detection models in terms of missed alarm rate (MAR) and false alarm rate (FAR). Therefore, it can well achieve the fault detection of large WT pitch connection bolts.

Keywords: Class-Imbalance; SMOTE; XGBoost; Pitch Connection Bolt; Oversampling; Fault Detection; Artificial Intelligence; Big Data Analytics; Decision Making (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401473

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