Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention


  • Guojian Li SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
  • Jian Wang SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
  • Yingwu Qin Mengdong Concord New Energy Company, Tongliao, Inner Mongolia, 028000, China
  • Xuefeng Bai SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
  • Yuhan Jiang SPIC Nei Mongol Energy CO., LTD., Tongliao, Inner Mongolia, 028000, China
  • Yi Deng Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China
  • Zhiyuan Ma Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China
  • Mengnan Cao Shanghai Energy Technology Development CO., LTD., Shanghai, 200233, China




Enhanced visual field; Convolutional neural network; Wind farms; Fault diagnosis; Prevention model


Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.


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