An interpretable power system transient stability assessment method with expert guiding neural regression tree

Deep neural networks have achieved great success in power system transient stability assessment (TSA); however, their black-box nature severely limits industrial application. Although numerous studies have explored the interpretability of network solutions, several challenges remain unsolved: (1) the discrepancy between widely accepted power system knowledge and the generated interpretive rules being large, (2) achieving an optimal balance between accuracy and interpretability remains difficult. To address these issues, an interpretable TSA model with E xpert guiding N eural R egression T ree (ENRT) is proposed. In ENRT, a specialized nonlinear regression tree is introduced to approximate the deep learning-based TSA model, with its decision paths simulating the black-box reasoning process of the neural network. The nonlinear terms in the tree model are extracted from the power flow calculation model with expert knowledge, ensuring that the generated interpretive rules better align with human cognition. By regularizing the neural network with the average decision depth of the nonlinear regression tree, we establish a connection between the neural network and the tree model at the training level, achieving a better trade-off between accuracy and interpretability. Our experiments across multiple TSA tasks demonstrate that ENRT effectively balances accuracy and interpretability, achieving a 97.1% fidelity between the neural network and the tree model, along with an accuracy of 94.3%. Furthermore, the generated interpretive rules capture nonlinear terms with clear physical significance, aligning more closely with power system knowledge.