WIND TURBINE BLADE ICING FAULT EARLY WARNING BASED ON BIRCH-MSSCN MODEL

Li Lianbing; Luo Wei; Cheng Qing; Su Wenyong; Chen Yece; Lu Zhihui

doi:10.19912/j.0254-0096.tynxb.2025-0010

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 341-349. DOI: 10.19912/j.0254-0096.tynxb.2025-0010

WIND TURBINE BLADE ICING FAULT EARLY WARNING BASED ON BIRCH-MSSCN MODEL

Li Lianbing¹, Luo Wei¹, Cheng Qing², Su Wenyong², Chen Yece², Lu Zhihui²

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Abstract

To provide early warnings for blade icing faults, a BIRCH-MSSCN model is proposed, based on Balanced Iterative Reducing and Clustering using Hierarchies （BIRCH）, Mel spectrograms, Siamese networks, and the Convolutional-based Multi-scale Attention Mechanism （CBAM）. The model achieves early fault warnings for blade icing through labeling pre-icing states, down-sampling normal data, measuring similarity using the Siamese network, and selecting features with the CBAM attention mechanism. Comparative analysis results indicate that while maintaining low computational costs, the model can accurately predict icing faults up to 42 minutes in advance. This model provides more reliable early warning information for the efficient and stable operation of wind turbines, showcasing significant application value.

Key words

fault diagnosis / wind turbine blades / icing / neural network / attention mechanism / sample imbalance

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Li Lianbing, Luo Wei, Cheng Qing, Su Wenyong, Chen Yece, Lu Zhihui. WIND TURBINE BLADE ICING FAULT EARLY WARNING BASED ON BIRCH-MSSCN MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 341-349 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0010

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