Fault diagnosis of photovoltaic arrays with different degradation levels based on cross-domain adaptive generative adversarial network

Peijie Lin, Feng Guo, Yaohai Lin, Shuying Cheng, Xiaoyang Lu, Zhicong Chen and Lijun Wu

Applied Energy, 2025, vol. 386, issue C, No S0306261925003083

Abstract: Recently, promising progresses have been made in photovoltaic (PV) arrays fault diagnosis (FD) due to the importance of operation and maintenance of PV power plants. However, PV arrays inevitably experience gradual degradation due to the complexity of operating conditions, resulting in domain shift of output data, which has a significant negative impact on the performance of FD. To address these problems, this study proposes a two-stage cross-domain, i.e., adaptive generative adversarial network deep learning approach for PV arrays FD under different degradation levels. In the first stage, the Normal data from the source domain (PV arrays without performance degradation) is utilized for training. Then, the Maximum Mean Discrepancy (MMD) loss is introduced to the fault generators in adversarial training to produce high-level feature representations of source domain fault data. In the second stage, identical training steps are used to guide the fault generators. Specifically, Normal data from the target domain i.e., PV arrays with performance degradation, is utilized to generate fault data features that are consistent with the target domain features. Then, the cross-domain adaptive FD model can be trained by using generated fault data features. The proposed model can not only learn the relationship from the different types of data, but also utilize target domain PV array data under healthy conditions to manually generate fake samples for cross-domain adaptive FD. Experimental results show that the Precision of the proposed model in the two tasks is 98.34 % and 92.93 %, with Recall is 98.23 % and 94.13 %, F1-Score is 0.9823 and 0.9274, all of which are better than those of the comparison models.

Keywords: Photovoltaic arrays; Fault diagnosis; Domain adaptation; Adversarial networks; Generative models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125578

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