ABNORMAL CONDITION MONITORING OF WIND TURBINE BASED ON VISION TRANSFORMER MULTI-MODEL FUSION

Xiang Ling; Gao Xin; Yao Qingtao; Su hao; Hu Aijun; Cheng Lifeng

doi:10.19912/j.0254-0096.tynxb.2023-2117

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 522-529. DOI: 10.19912/j.0254-0096.tynxb.2023-2117

ABNORMAL CONDITION MONITORING OF WIND TURBINE BASED ON VISION TRANSFORMER MULTI-MODEL FUSION

Xiang Ling, Gao Xin, Yao Qingtao, Su hao, Hu Aijun, Cheng Lifeng

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Abstract

In order to realize the abnormal state monitoring of wind turbines and use them for fault diagnosis and routine maintenance, a new monitoring method is proposed in this paper, which is based on the fusion of Vision Transformer model and long short-term memory （LSTM） network, which can effectively identify the operating status of wind turbines. Firstly, the box plot method and Spearman correlation analysis were used to preprocess the original SCADA data, remove the invalid data and select the input parameters. Then, the Vision Transformer prediction model fused with LSTM was constructed, and the KL divergence in statistics was introduced as the detection index to calculate and analyze the predicted value and the real value of the target parameter. Finally, the kernel density estimation is used to determine the safety threshold, and the abnormal state of the wind turbine is identified according to whether the detection index exceeds the safety threshold. The model was applied to a wind farm in North China for case analysis, and compared with other deep Xi models. The results show that the proposed method can better identify the abnormal state of wind turbines than other models.

Key words

wind turbine / condition monitoring / LSTM / ViT / KL divergence

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Xiang Ling, Gao Xin, Yao Qingtao, Su hao, Hu Aijun, Cheng Lifeng. ABNORMAL CONDITION MONITORING OF WIND TURBINE BASED ON VISION TRANSFORMER MULTI-MODEL FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 522-529 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2117

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