MULTI-TYPE COMPOSITE FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAY BASED ON EVOLUTIONARY ALGORITHM AND LSTM

Bai Muke; Qian Zhiyan; Duan Xiangjun; Li Jia

doi:10.19912/j.0254-0096.tynxb.2024-1927

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 625-634. DOI: 10.19912/j.0254-0096.tynxb.2024-1927

MULTI-TYPE COMPOSITE FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAY BASED ON EVOLUTIONARY ALGORITHM AND LSTM

Bai Muke, Qian Zhiyan, Duan Xiangjun, Li Jia

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Abstract

To improve the accuracy of fault diagnosis in photovoltaic arrays, a strategy is proposed to optimize the Long Short-Term Memory network （LSTM） using the Cauchy Gaussian Mutation Artificial Rabbit Algorithm （CGARO） to achieve efficient diagnosis of multiple types of composite faults in photovoltaic arrays. Firstly, in order to the problem of getting trapped in local optima in the artificial rabbit optimization （ARO）, a Cauchy Gaussian mutation artificial rabbit algorithm is proposed. Comparative analysis was conducted between CGARO and ARO, Sparrow Search Algorithm （SSA）, and Grey Wolf Optimization Algorithm（GWO） to verify the effectiveness of CGARO algorithm. Then, the CGARO algorithm was optimized to optimize the parameters and learning rate of LSTM, and a CGARO-LSTM photovoltaic array fault diagnosis model was established. Based on four types of single faults and three types of composite faults, the CGARO-LSTM model was compared with LSTM, SSA-LSTM, GWO-LSTM, and ARO-LSTM. The results showed that the CGARO-LSTM model had better performance, with an accuracy of 97.75%, significantly improving the accuracy of photovoltaic array fault diagnosis.

Key words

photovoltaic arrays / LSTM / evolutionary algorithm / fault diagnosis / diagnosis model / type of faults / accuracy

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Bai Muke, Qian Zhiyan, Duan Xiangjun, Li Jia. MULTI-TYPE COMPOSITE FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAY BASED ON EVOLUTIONARY ALGORITHM AND LSTM[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 625-634 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1927

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