An efficient fault diagnosis method combining multi-angle feature expansion and visual image neural networks for solar photovoltaic modules

Qiao Liu, Haotian Shi, Yuyu Zhu, Lei Chen, Manlu Liu, Wen Cao and Qi Huang

Energy, 2025, vol. 333, issue C

Abstract: Fault diagnosis of photovoltaic arrays is a key link to ensure stable operation of photovoltaic systems and improve power generation efficiency, and timely and accurate fault diagnosis becomes particularly important. A photovoltaic fault diagnosis method, termed MA-GCT, is proposed based on multi-angle feature expansion and visual image-based deep learning. Voltage, current, and power signals are enhanced using multi-angle features to enrich fault information. The sequences are subsequently converted into two-dimensional images through gramian angular field (GAF) transformation, enabling the joint representation of temporal dynamics and structural characteristics. A hybrid CNN-transformer architecture is developed to leverage both local feature extraction and global dependency modeling. Experimental validation on multi-class fault scenarios, including simulations and real-fault data, demonstrates classification accuracy, recall, and F1 scores of no less than 96.7 %, 95.0 %, and 95.8 %, respectively. The proposed model consistently achieved a fault diagnosis accuracy exceeding 99 % across both simulation and experimental evaluations, demonstrating its robust ability to extract discriminative features and classify diverse fault types.

Keywords: Photovoltaic fault diagnosis; Feature expansion; Gram angular field; CNN-Transformer networks (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031263

DOI: 10.1016/j.energy.2025.137484

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