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<p>Aiming at the problem that the fault diagnosis of photovoltaic array is interfered by harsh environments, and the single model is not effective in extracting effective feature information, which leads to low diagnosis accuracy, a fault diagnosis method for photovoltaic arrays based on model fusion is proposed. In this method, a multi-channel one-dimensional convolutional neural network is designed to capture multi-scale information, thereby improving feature representation. LSTM, AdaBoost, PNN, and logistic regression methods were used to construct the stacking model to predict and classify the fault types of photovoltaic arrays. Experimental results show that the accuracy of fault classification of the proposed method reaches 96.4%, which verifies the effectiveness of the proposed method.</p>

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Research on fault diagnosis method for photovoltaic array based on model fusion

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Fuyan Guo,
  • Weijiang Fu,
  • Yue Wang,
  • Jiao Chen

摘要

Aiming at the problem that the fault diagnosis of photovoltaic array is interfered by harsh environments, and the single model is not effective in extracting effective feature information, which leads to low diagnosis accuracy, a fault diagnosis method for photovoltaic arrays based on model fusion is proposed. In this method, a multi-channel one-dimensional convolutional neural network is designed to capture multi-scale information, thereby improving feature representation. LSTM, AdaBoost, PNN, and logistic regression methods were used to construct the stacking model to predict and classify the fault types of photovoltaic arrays. Experimental results show that the accuracy of fault classification of the proposed method reaches 96.4%, which verifies the effectiveness of the proposed method.