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摘要
Aiming at the problems of weak generalization and poor interpretability of existing intelligent methods in photovoltaic array fault detection and diagnosis，an interpretable intelligent integration method is proposed. The feature mining is performed on the collected output time-series of voltage and current waveforms of the photovoltaic array，and multiple mature intelligent algorithms that have been applied to photovoltaic fault diagnosis are used as different base learners and meta learners to construct a Stacking ensemble learning model that combines the advantages of different intelligent algorithms and is more generalized. Then，taking the Shapley additive explanation method as the overall framework，combined with the local approximate interpretable method，the model training process and results are explained and analyzed. By obtaining the contributions of each feature，analyzing the decision-making mechanism of the integrated model，and understanding how to diagnose it，the reliability and credibility of the model are improved. The experimental results of case study show that the proposed interpretable intelligent integration method achieves high-precision fault diagnosis in testing on datasets of different sizes. The interpretability results of the model indicate that the mapping of fault features and diagnostic results established by the intelligent integration model follows physical insights，enhancing the credibility and transparency of the intelligent method. ? 2024 Electric Power Automation Equipment Press.
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