WIND TURBINE FAULT WARNING SYSTEM BASED ON CYCLIC DBSMOTE AND MULTI-HEAD SELF-ATTENTION MECHANISM

Qi Fangzhong; Cai Ruanhao; Cao Jian

doi:10.19912/j.0254-0096.tynxb.2025-0109

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 772-782. DOI: 10.19912/j.0254-0096.tynxb.2025-0109

WIND TURBINE FAULT WARNING SYSTEM BASED ON CYCLIC DBSMOTE AND MULTI-HEAD SELF-ATTENTION MECHANISM

Qi Fangzhong^1,2, Cai Ruanhao¹, Cao Jian¹

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Abstract

This study proposes a hybrid fault early-warning model and architecture for wind turbines, integrating the advantages of traditional convolutional neural networks （CNN） and long short-term memory networks （LSTM）, coupled with a cyclic DBSMOTE algorithm and a multi-head attention mechanism. First, a cyclic DBSMOTE algorithm is developed to balance highly imbalanced wind turbine operational datasets, mitigating the bias caused by extreme class distribution. In the hybrid early-warning model, CNN and LSTM are employed to extract spatial and temporal features from the balanced data, respectively, enhancing the depth of data mining. Additionally, a multi-head attention mechanism is introduced to prioritize critical temporal-spatial correlations, significantly improving the model’s training accuracy and early-warning performance. Through hyperparameter optimization, the model achieves fault early-warning capabilities with a lead time of up to 32 hours in experimental validation. Further validation via ablation studies, comparative experiments, and ten-fold cross-validation demonstrates that the proposed hybrid model exhibits superior effectiveness and stability in wind turbine fault early warning.

Key words

wind turbines / fault early warning / deep learning / cyclic DBSMOTE / multi-Head self-attention mechanism

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Qi Fangzhong, Cai Ruanhao, Cao Jian. WIND TURBINE FAULT WARNING SYSTEM BASED ON CYCLIC DBSMOTE AND MULTI-HEAD SELF-ATTENTION MECHANISM[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 772-782 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0109

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