一种基于卷积神经网络和长短期记忆网络的光伏系统故障辨识方法

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Abstract As the installed capacity of photovoltaic power generation continues to rise, how to detect and solve the faults and abnormalities of the photovoltaic modules in time to reduce energy loss and improve the power generation efficiency of photovoltaic systems has become a significant task. The characteristic differences between the I-V curves of photovoltaic arrays in different fault states are studied in this paper. The I-V curves are directly used as the input for fault diagnosis. On these grounds, a photovoltaic system fault identification method based on convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed in this paper. Experimental results show that this method can not only identify single faults like short circuit, partial shading, abnormal aging and so on, but also effectively identify the simultaneous existence of hybrid faults.

Key words： photovoltaic power system fault diagnosis I-V curves convolutional neural network (CNN) long short-term memory (LSTM) network

Received: 02 September 2021

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	Articles by authors
	TU Yanzhao
	GAO Wei
	YANG Gengjie

Cite this article:

TU Yanzhao,GAO Wei,YANG Gengjie. A photovoltaic system fault identification method based on convolutional neural network and long short-term memory network[J]. Electrical Engineering, 2022, 23(2): 48-54.

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http://dqjs.cesmedia.cn/EN/Y2022/V23/I2/48

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