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In order to realize accurate diagnosis of large-scale PV fault data, a fault diagnosis method based on feature extraction was proposed to realize accurate diagnosis of different types of faults. By analyzing the output characteristic curves of different faults, the characteristic quantities reflecting different fault characteristics were extracted. PCA (Principal Component Analysis) was used to downsize and initially detect the fault data, and the CatBoost algorithm was used to classify the fault data, and the fault diagnosis model based on PCA-CatBoost algorithm was established. Through the MATLAB simulation experiment analysis, it was verified that the algorithm model can effectively improve diagnostic accuracy.

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Fault Diagnosis of Photovoltaic Modules Based on Feature Extraction

  • 会议论文

  • 发表日期：

    2025年

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  • Xueqi Wang,
  • Lixia Cao

摘要

In order to realize accurate diagnosis of large-scale PV fault data, a fault diagnosis method based on feature extraction was proposed to realize accurate diagnosis of different types of faults. By analyzing the output characteristic curves of different faults, the characteristic quantities reflecting different fault characteristics were extracted. PCA (Principal Component Analysis) was used to downsize and initially detect the fault data, and the CatBoost algorithm was used to classify the fault data, and the fault diagnosis model based on PCA-CatBoost algorithm was established. Through the MATLAB simulation experiment analysis, it was verified that the algorithm model can effectively improve diagnostic accuracy.