A FAULT DIAGNOSIS METHOD FOR PHOTOVOLTAIC ARRAY VIA BOA-SAE-EELM

Chen Shiqun; Yang Gengjie; Gao Wei

doi:10.19912/j.0254-0096.tynxb.2020-0763

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 154-161. DOI: 10.19912/j.0254-0096.tynxb.2020-0763

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

A FAULT DIAGNOSIS METHOD FOR PHOTOVOLTAIC ARRAY VIA BOA-SAE-EELM

Chen Shiqun, Yang Gengjie, Gao Wei

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Abstract

The nonlinear characteristics of the output of the photovoltaic（PV） array and the maximum power point tracking algorithm will affect the operation of PV array protection equipment. In order to identify correctly the operating status of PV array, a novel fault diagnosis method that combines Bayesian optimization algorithm（BOA）, stacked autoencoder（SAE）, and ensemble extreme learning machine（EELM） is proposed in this study. First, the time sequence waveform of PV array is standardized. Then, SAE is used to automatically extract the features of the standardized time sequence waveforms, and an EELM model is trained for fault classification. Finally, BOA optimizes the overall diagnosis model. The experimental results show that the proposed method has 98.40% and 98.10% fault diagnosis accuracy for simulation and experiment, respectively, which is better than that of backpropagation neural network, support vector machine, random forest, etc.

Key words

photovoltaic array / fault diagnosis / stacked autoencoder（SAE） / extreme learning machine（ELM） / Bayesian optimization algorithm（BOA） / sequence waveform

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Chen Shiqun, Yang Gengjie, Gao Wei. A FAULT DIAGNOSIS METHOD FOR PHOTOVOLTAIC ARRAY VIA BOA-SAE-EELM[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 154-161 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0763

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