Explainable fault diagnosis using discrete grey wolf optimization algorithm for photovoltaic system
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Date
2025
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Abstract
The present article introduces the discrete grey wolf optimization algorithm
(DGWOA), a novel variant of the grey wolf optimizer (GWO). DGWOA
integrates discrete optimization techniques with explainable artificial
intelligence (XAI) methodologies. This approach aims to overcome
limitations associated with traditional fault diagnosis methods, such as
limited accuracy in identifying complex patterns and low interpretability.
Furthermore, it mitigates early convergence problems commonly
encountered in optimization algorithms and enhances adaptability to discrete
classification challenges. The DGWOA algorithm is designed to generate
interpretable classification rules for fault detection through a stochastic
search strategy. The explainability provided by the model not only enhances
decision-making transparency but also improves diagnostic efficiency and
predictive accuracy. The proposed algorithm was evaluated using a
photovoltaic system dataset and benchmarked against established rule-based
classifiers. DGWOA consistently achieved a classification accuracy of
99.48% and a precision of 100%, demonstrating its effectiveness in
enhancing fault detection. Moreover, the interpretability of the generated
classification rules contributes to the generation of outcomes that are both
actionable and comprehensible to decision-makers.