PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS BASED ON IHBA-BiLSTM

Yu Zhongming; Zhang Yu; Lu Ketong; Chen Keyu; Liu Zhijian; Dai Xin

doi:10.19912/j.0254-0096.tynxb.2024-1833

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 122-131. DOI: 10.19912/j.0254-0096.tynxb.2024-1833

PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS BASED ON IHBA-BiLSTM

Yu Zhongming¹, Zhang Yu¹, Lu Ketong¹, Chen Keyu¹, Liu Zhijian¹, Dai Xin²

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Abstract

To improve the accuracy of fault diagnosis for photovoltaic （PV） arrays, this study proposes a hybrid fault diagnosis model combining the improved honey badger algorithm（IHBA） with the bidirectional long short-term memory （BiLSTM） network. Different from feature engineering oriented to power prediction, this paper focuses on fault identification, and systematically extracts three types of features covering basic, discrete and distributional statistics from the current-voltage and power-voltage characteristic curves of PV arrays, forming a 10-dimensional comprehensive feature vector.Aiming at the defects of the original Honey Badger Algorithm such as premature convergence and insufficient search efficiency, the IHBA is improved in three aspects: Tent chaos mapping is adopted to optimize the initial population distribution; a dynamic adaptive control factor is designed to balance the search process; and the pinhole imaging reverse learning strategy is introduced to enhance the global optimization ability. Benchmark function tests demonstrate that the IHBA outperforms the comparative algorithms in both convergence speed and solution accuracy. On this basis, the IHBA is utilized to automatically optimize the hyperparameters of the BiLSTM network, overcoming the blindness of manual parameter tuning and significantly enhancing the model’s modeling capability and generalization for high-dimensional nonlinear fault features. Finally, on the simulation dataset containing five operational states including normal, open-circuit, short-circuit, partial shading and aging, the IHBA-BiLSTM model achieves a fault diagnosis accuracy of 97.1014%. Its performance comprehensively surpasses that of the comparative models such as support vector machine （SVM）, extreme learning machine （ELM）, long short-term memory （LSTM） network and models integrated with other intelligent optimization algorithms, confirming that the proposed method possesses both high precision and strong robustness in the diagnosis of multiple types of faults in PV arrays.

Key words

fault detection / feature extraction / learning algorithms / photovoltaic array / pinhole imaging strategy / bidirectional long short-term memory neural network

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Yu Zhongming, Zhang Yu, Lu Ketong, Chen Keyu, Liu Zhijian, Dai Xin. PHOTOVOLTAIC ARRAY FAULT DIAGNOSIS BASED ON IHBA-BiLSTM[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 122-131 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1833

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