An interpretable uncertainty quantification mechanism based photovoltaic array fault diagnosis and unknown fault incremental model

Although deep learning methods have demonstrated promising performance in photovoltaic (PV) fault diagnosis, the black-box nature of deep learning models poses challenges. This means that conventional models cannot provide trustworthy information for reasonable explanations in diagnosing PV faults. At the same time, when faced with the pattern characteristics of unknown faults, the model is prone to confusion, which can lead to misdiagnosis. To address these challenges, this paper proposes a PV fault diagnosis model that combines an interpretable uncertainty quantification with class incremental learning, named the Class Incremental Uncertainty Fusion Network (CIUFN). This model not only achieves precise diagnosis for PV fault types but also quantifies and analyzes the uncertainty in diagnostic results, thereby providing measurable indicators for the credibility of the model. Meanwhile, taking the uncertainty information as an entry point, we further studied the incremental learning mechanism for unknown faults to achieve accurate identification of unknown fault types. Experimental results demonstrate that the CIUFN model exhibits excellent diagnostic accuracy and interpretability across various PV fault scenarios and maintains outstanding accuracy even when the number of PV fault categories increases.