PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS METHOD BASED ON SV-CBDE MODEL

Li Yang; Guo Chunmei; Tang Junhao; You Yuwen; He Zhonglu; Wang Leilei

doi:10.19912/j.0254-0096.tynxb.2024-0993

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 227-236. DOI: 10.19912/j.0254-0096.tynxb.2024-0993

PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS METHOD BASED ON SV-CBDE MODEL

Li Yang¹, Guo Chunmei¹, Tang Junhao¹, You Yuwen¹, He Zhonglu¹, Wang Leilei²

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Abstract

In order to achieve accurate fault diagnosis and improve the power generation efficiency of photovoltaic array systems, this paper proposes a SV-CBDE diagnosis model that integrates a hybrid model of convolutional neural network and bidirectional gated recurrent unit network, and a hybrid model of deep belief network and extreme learning machine with a soft voting method. Taking the P-V curve data of the photovoltaic array as input, the two models are used to diagnose the samples separately, and then the probabilities of the diagnostic results output from the two diagnosis models are weighted and averaged to derive the final diagnostic results and thus the type of faults are determined, so as to improve the accuracy of the photovoltaic array fault diagnosis. The proposed model is evaluated on the same dataset against four fault diagnosis models （CNN, CNN-LSTM, CNN-BiGRU, and DBN-ELM）. The results indicate that the integrated model proposed in this paper exhibits higher accuracy, recall, precision, and F1-score compared to the other four models in diagnosing inter-string open circuits, locally generated shadows in components, inter-component short circuits, photovoltaic array aging, and three types of concurrent faults, demonstrating its superior fault identification capability.

Key words

photovoltaic array / fault diagnosis / integrate / diagnosis model / type of faults / accuracy

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Li Yang, Guo Chunmei, Tang Junhao, You Yuwen, He Zhonglu, Wang Leilei. PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS METHOD BASED ON SV-CBDE MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 227-236 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0993

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