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Fault Diagnosis of Photovoltaic Array Based on Improved Honey Badger Optimization Algorithm

Author

Listed:
  • Zhuo Guo

    (Faculty of Electrical and Control Engineering, Liaoning Technical University, Fuxin 123000, China)

  • Yuanyuan Chang

    (School of Economics and Management, Liaoning Finance and Trade College, Huludao 125105, China)

  • Yanhong Fang

    (Faculty of Electrical and Control Engineering, Liaoning Technical University, Fuxin 123000, China)

Abstract

A photovoltaic array fault diagnosis method based on an improved honey badger optimization algorithm is proposed to improve the accuracy of photovoltaic array fault diagnosis. Firstly, analyze the current and power output characteristic curves of the photovoltaic array under different states, and construct a preliminary set of 10 dimensional fault feature vectors. Secondly, the feature vector set is ranked in importance using the random forest algorithm, and then input into support vector machines, long short-term memory, and bidirectional long short-term memory neural networks to obtain the optimal combination of the base model and the number of features. Then, the honey badger optimization algorithm was improved by combining Tent chaotic mapping column measurement, improved control factors, and pinhole imaging strategy, and compared with other optimization algorithms to demonstrate its effectiveness in optimization ability, stability, and convergence speed. Finally, by combining the improved honey badger optimization algorithm with the optimal base model and number of features, the problem of hyperparameter setting in the base model was effectively solved. The experimental results show that the fault diagnosis accuracy of the proposed photovoltaic array fault diagnosis model is 97.1014%, which is superior to other models and verifies the effectiveness of the proposed method.

Suggested Citation

  • Zhuo Guo & Yuanyuan Chang & Yanhong Fang, 2025. "Fault Diagnosis of Photovoltaic Array Based on Improved Honey Badger Optimization Algorithm," Energies, MDPI, vol. 18(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:841-:d:1588701
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    References listed on IDEAS

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    1. Zhang, Mingyue & Han, Yang & Wang, Chaoyang & Yang, Ping & Wang, Congling & Zalhaf, Amr S., 2024. "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, Elsevier, vol. 375(C).
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