Online health diagnosis of PV modules based on T-S fuzzy neural network

WANG Fuzhong; HOU Ao; ZHANG Li

doi:10.13873/J.1000-9787(2021)12-0153-04

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Online health diagnosis of PV modules based on T-S fuzzy neural network

更新时间：2025-09-11

- Online health diagnosis of PV modules based on T-S fuzzy neural network
- Issue 12, Pages: 153-156+160(2021)
- 作者机构：
  
  河南理工大学电气工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)12-0153-04
  CLC： TM615;TP183
- Published：2021
- 稿件说明：
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WANG Fuzhong, HOU Ao, ZHANG Li. Online health diagnosis of PV modules based on T-S fuzzy neural network[J]. 2021, (12): 153-156+160.
DOI：

WANG Fuzhong, HOU Ao, ZHANG Li. Online health diagnosis of PV modules based on T-S fuzzy neural network[J]. 2021, (12): 153-156+160. DOI： 10.13873/J.1000-9787(2021)12-0153-04.

摘要

为了能够实时掌握光伏组件的健康状况

提出一种基于T-S模糊神经网络的在线健康诊断模型。首先

分析了常见光伏组件异常工况下的电气输出并选取特征向量;其次

利用模糊C均值(FCM)及自适应LM算法对T-S模糊神经网络进行了优化;最后

通过仿真验证了模型的有效性和可行性。仿真结果表明:本文诊断方法的诊断正确率达到了94%

诊断精度与收敛速度都优于传统T-S模糊神经网络模型。

Abstract

In order to grasp health status of photovoltaic（PV）modules in real-time

an online health diagnosis model based on Takagi-Sugeno（T-S） fuzzy neural network is proposed.Firstly

electrical output of common PV modules under abnormal operating conditions is analyzed and feature vectors are selected.Then use fuzzy C-means（FCM）and adaptive LM algorithm to optimize the T-S fuzzy neural network.Final simulation experiment proves the feasibility of the model.The results show that the diagnostic accuracy of this diagnostic method reaches 94 %

and the diagnostic precision and convergence speed prior to the traditional T-S fuzzy neural network model.

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