Fault Diagnosis Method of Photovoltaic Array Based on CNN-GRU

LU Wanjie; MA Pan

doi:10.13296/j.1001-1609.hva.2026.05.021

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Fault Diagnosis Method of Photovoltaic Array Based on CNN-GRU

Research & Analysis | 更新时间：2026-05-13

- Fault Diagnosis Method of Photovoltaic Array Based on CNN-GRU
- High Voltage Apparatus Vol. 62, Issue 5, Pages: 180-185(2026)
- 作者机构：
  
  辽宁工程技术大学，辽宁葫芦岛 125105
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.05.021
  CLC：
- Received：05 October 2025，
  
  Revised：2025-12-20，
  
  Published：16 May 2026
- 稿件说明：
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LU Wanjie, MA Pan. Fault Diagnosis Method of Photovoltaic Array Based on CNN-GRU[J]. High Voltage Apparatus, 2026, 62(5): 180-185.
DOI：

LU Wanjie, MA Pan. Fault Diagnosis Method of Photovoltaic Array Based on CNN-GRU[J]. High Voltage Apparatus, 2026, 62(5): 180-185. DOI： 10.13296/j.1001-1609.hva.2026.05.021.

摘要

针对光伏阵列故障诊断识别率较低的问题，文中提出一种基于卷积神经网络（convolutional neural network）与门控循环单元（gate recurrent unit）相结合的光伏阵列故障诊断方法。首先利用MATLAB/Simulink软件搭建光伏阵列的仿真模型，分别模拟出短路、开路、老化、遮阴4种典型故障，并记录其特征参数的数据；接着将构建好的特征集划分成训练集与测试集输入至CNN-GRU识别模型中进行故障类型的诊断。并分别与CNN、PNN、GRU等识别模型进行对比。通过仿真实验可知，文中提出的诊断模型可以更加准确且快速的完成诊断，诊断率高达99.5%，具有较好的故障识别能力。

Abstract

As for the problem of low fault diagnosis recognition rate of photovoltaic system

in this paper a fault diagnosis method for photovoltaic system based on the combination of convolutional neural network and gate recurrent unit is proposed. First

MATLAB/Simulink software is used to set up the simulation model of photovoltaic system

such four typical faults as short circuit

open circuit

aging and shading are simulated respectively

and the data of its characteristic parameters is recorded. Then

the constructed feature set is classified into the training set and testing set and input into the CNN-GRU recognition model for fault type diagnosis. Comparison with such recognition models as CNN

PNN and GRU is made. It is known through simulation experiments that the diagnostic model proposed in the paper can complete the diagnosis more accurately and quickly

with a diagnosis rate up to 99.5% and also good fault recognition ability.

关键词

Keywords

references

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