Self-Correcting-Guided Generalized Contrastive Learning Framework for Small-Sample PV Fault Diagnosis With Cloud-Edge Collaboration

Intelligent fault diagnosis of photovoltaic (PV) arrays in small-sample scenarios remains challenging due to poor model accuracy and generalization. Existing methods fail to simultaneously address issues of varied operation conditions and insufficient samples, leading to the limited applicability of models built by few-shot learning. In addition, factors, such as data transmission and computation costs, also need to be considered. Therefore, this article proposes a cloud-edge collaborative self-correcting-guided generalized contrastive learning framework for small-sample PV fault diagnosis. First, an end-to-end self-correcting model is proposed to eliminate the influence of variable environments. Then, a self-correcting scheme is integrated with contrastive learning to achieve model generalization, and a type screening method is designed to improve model accuracy. Furthermore, a fast fault filtering mechanism is proposed to enhance the algorithm efficiency with cloud-edge collaboration. Both simulation and real data are utilized to validate the proposed method.