首页|Fault Diagnosis Based on Interpretable Convolutional Temporal-spatial Attention Network for Offshore Wind Turbines

Fault Diagnosis Based on Interpretable Convolutional Temporal-spatial Attention Network for Offshore Wind Turbines

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Fault diagnosis(FD)for offshore wind turbines(WTs)are instrumental to their operation and maintenance(O&M).To improve the FD effect in the very early stage,a con-dition monitoring based sample set mining method from super-visory control and data acquisition(SCADA)time-series data is proposed.Then,based on the convolutional neural network(CNN)and attention mechanism,an interpretable convolutional temporal-spatial attention network(CTSAN)model is pro-posed.The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by:① a convolution feature extraction module to extract features based on time intervals;② a spatial attention module to ex-tract spatial features considering the weights of different fea-tures;and ③ a temporal attention module to extract temporal features considering the weights of intervals.The proposed CT-SAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-under-standable form of the temporal-spatial attention weights.The ef-fectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China.

Offshore wind turbine(WT)gearboxfault di-agnosis(FD)attention mechanisminterpretabilitytemporal-spatial feature

Xiangjing Su、Chao Deng、Yanhao Shan、Farhad Shahnia、Yang Fu、Zhaoyang Dong

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Engineering Research Center of Offshore Wind Technology Ministry of Education,Shanghai University of Electric Power,Shanghai 200090,China

Offshore Wind Power Research Institute,Shanghai University of Electric Power,Shanghai 200090,China

Yantai Power Supply Company,State Grid Shandong Electric Power Co.,Ltd.,Yantai 264001,China

School of Engineering and Energy,Murdoch Universi-ty,Perth WA 6150,Australia

School of Electrical and Electronic Engineering,Nan-yang Technological University,Singapore 639798,Singapore

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2024

10.35833/MPCE.2023.000606

现代电力系统与清洁能源学报(英文版)

现代电力系统与清洁能源学报(英文版)

ISSN：
年,卷(期)：2024.12(5)