一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法

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Abstract

The failure of the photovoltaic (PV) array can cause energy loss and even cause power supply interruption or fire accident. Therefore, it is very important to identify the fault quickly. A new fast fault diagnosis method for PV array is proposed to quickly perceive the fault and the time of fault occurrence in this paper. Based on the analysis of the signal variation rule of PV array traditional faults, a prediction model of extreme learning machine (ELM) is investigated to train the power waveform during normal operation, which is adopted to predict the short-time power variation. Then Pearson's correlation coefficient of measured waveforms and predicted waveforms are calculated. If the PV array fails, the correlation coefficient will be below the threshold, thus identifying the occurrence of the fault. Experimental results show that the proposed method has strong ability of fault identification, and the accuracy reaches 99.37%. The fault identification time of the proposed method is only 4.355ms, and it can be used as the start-up method of PV array fault recording.

Key words： photovoltaic array fault recording extreme learning machine (ELM) Pearson's correlation coefficient

Received: 26 February 2021

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	Articles by authors
	CHEN Shiqun
	GAO Wei
	CHEN Xiaoqi
	TU Yanzhao
	YANG Yan

Cite this article:

CHEN Shiqun,GAO Wei,CHEN Xiaoqi等. A fast fault diagnosis method for photovoltaic array via extreme learning machine and Pearson's correlation coefficient[J]. Electrical Engineering, 2021, 22(10): 57-64.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I10/57

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