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Fault diagnosis of photovoltaic (PV) arrays is an essential task for improving the reliability and safety of a photovoltaic system (PVS). The PVS faults at the DC side are difficult to detect by traditional protective devices, which may reduce power conversion efficiency and even lead to safety matters and fire disaster. This study investigates a newly-designed fault diagnostic method for a PVS according to the following three steps. First, optimal fault features are extracted by analyzing I-V curves from different faults, including hybrid faults of the PVS under the standard test condition (STC). Moreover, the trust-region-reflective (TRR) deterministic algorithm combined with the particle-swarm-optimization (PSO) metaheuristic algorithm is proposed to standardize fault features into the ones under the STC. In addition, a multi-class adaptive boosting (AdaBoost) algorithm, which is the stage-wise additive modeling using multi-class exponential (SAMME) loss function based on the classification and regression tree (CART) as the weak classifier, is utilized to establish the fault diagnostic model. The effectiveness of the fault diagnostic model could long-term maintain by periodically updating the feature standardization equations to standardize the fault features into the ones under the STC. Various types of the PV modules are used to validate the generalization of the fault diagnostic method. Both the numerical simulations and experimental results show the accuracy and reliability of the proposed fault diagnostic method.

Newly-Designed Fault Diagnostic Method for Solar Photovoltaic Generation System Based on IV-Curve Measurement