RESEARCH ON FAULT DIAGNOSIS METHOD OF PV ARRAY BASED ON CLD-COA-ELM

Li Jingyun; Zhang Jian; Zhao Mi; Huang Yi

doi:10.19912/j.0254-0096.tynxb.2023-1468

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 632-640. DOI: 10.19912/j.0254-0096.tynxb.2023-1468

RESEARCH ON FAULT DIAGNOSIS METHOD OF PV ARRAY BASED ON CLD-COA-ELM

Zhang Jian¹, Zhao Mi¹, Huang Yi¹, Li Jingyun²

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Abstract

In this article, an improved Coati Optimization Algorithm is proposed to optimize the diagnosis method of Extreme Learning Machine to achieve accurate classification of PV array failures. Firstly, the output characteristics of PV panels failing in the array are analyzed and appropriate fault characteristics are selected. Secondly, Coati Optimization Algorithm is utilized to solve the optimal initial weights and thresholds by exploiting the stochasticity of the initial weights and thresholds of Extreme Learning Machine for PV array fault classification. Furthermore, both the randomness of the initial parameters and the limitation of global search ability of Coati Optimization Algorithm are optimized by circle chaotic mapping, levy flight and dynamic refractive inverse learning, which greatly reduces the training time and improves the optimization search accuracy of the algorithm. Finally, the classification performance of the failure diagnostic model will be validated using experimental failure data of PV array, the corresponding results indicate that the diagnostic model improves the fault classification accuracy to 100% and 98.33% for the training set and test set, respectively, which is superior to the traditional extreme learning machine, BP neural network, support vector machine and convolutional neural networks.

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PV panels / failure analysis / feature selection / supervised learning / extreme learning machine / improved coati optimization algorithm

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Zhang Jian, Zhao Mi, Huang Yi, Li Jingyun. RESEARCH ON FAULT DIAGNOSIS METHOD OF PV ARRAY BASED ON CLD-COA-ELM[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 632-640 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1468

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