Interpretable adaptive fault detection method for smart grid based on belief rule base

An effective fault detection strategy has always been the focus of smart grid system research. Fast and accurate fault detection is the basis for complex systems to maintain reliability and security. However, traditional fault detection methods often ignore the interpretability of the model while pursuing high detection accuracy. Complex models can usually provide higher detection accuracy, but often lack transparency and interpretability, making it difficult for operators to understand and trust the detection results of the model. Therefore, a new fault detection strategy based on an adaptive interpretable belief rule base (AI-BRB) is proposed. This method considers the adaptive updating of the search domain of the model accuracy to achieve the balance and optimization between the two conflicting objectives of the model interpretability and the detection accuracy. The fault detection model based on AI-BRB considers the interpretability of modeling, inference and optimization processes. In the optimization process, interpretability constraints are added to maintain the interpretability of the optimized model. In addition, in order to avoid falling into the local optimal solution in the optimization process, the search domain is updated adaptively according to the accuracy of the model, which improves the interpretability and robustness of the fault detection model. Finally, an example is given to prove that the proposed method can improve the accuracy of fault detection and the interpretability of the model compared with the existing methods.