Photovoltaic Rapid Shut-off Device Health Status Assessment Model Based on Multi-Modal Feature Fusion and Transfer Learning

Photovoltaic rapid shut-off devices operate for a long time in outdoor high-temperature, high-irradiation and humid-heat environments. Their early degradation characteristics are often weak and vary widely across sites. Traditional diagnostic methods based on a single electrical or thermal imaging signal are difficult to meet actual operation and maintenance needs. Therefore, this study proposes a photovoltaic rapid shutdown device health status assessment model based on multi-modal feature fusion and transfer learning. The model improves the complementary expression between different modalities through weak mode enhancement and cross-modal attention mechanisms, and it also uses a distribution alignment strategy to alleviate cross-site feature drift. Experiments showed that this method significantly improved recognition performance in multi-modal fusion experiments. The F1 value of the early fusion model increased from 82.21% to 89.54%, and the accuracy of the weak mode complete enhancement scheme increased from about 74% to nearly 86%, and remained the best in the four types of ablation comparisons. Furthermore, in the noise perturbation experiment, when the noise intensity reached 0.4, it still maintained an AUC of 0.89, which was 5–8% higher than the comparative method on average. At the same time, the performance retention rate still exceeded 89% when the mode was missing 30%, showing more stable robustness. The study verifies the stability of the proposed method under the conditions of weak degradation identification, noise immunity, and mode loss, and provides an effective technical approach for photovoltaic equipment health monitoring.