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Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis
Power Generation Technology Vol. 44, Issue 6, Pages: 824-832(2023)
Power Generation and Environmental Protection|Updated：2025-06-06
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Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis
- “In the field of wind turbine fault diagnosis, experts have proposed a diagnostic method based on wide convolutional deep neural networks, which effectively extracts fault features through signal preprocessing and PCA selection of data channels, providing a solution for wind turbine fault diagnosis.”
DOI：10.12096/j.2096-4528.pgt.22178
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Related Author
HU Weifei
FAN Jia
LU Chunbo
LIU Xu
MO Haotian
YAN Xiangyu
FANG Jianhao
WANG Lingzhi
Related Institution
New Energy Branch of Huadian (Ningxia) Energy Co., Ltd., Yinchuan 750000, Ningxia Autonomous Region
College of Mechanical Engineering, Zhejiang University
Huadian Electric Power Research Institute Co., Ltd.
School of Automation, Xi’an University of Posts and Telecommunications
College of Energy Engineering, Zhejiang University
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