Interpretation of Stability Assessment Machine Learning Models Based on Shapley Value

Machine learning is a promising method to solve the stability assessment problems of modern complex power systems. The interpretability of machine learning models is fundamental for trusting the prediction and deploying the models on engineering application. In this paper, a model-agnostic interpretation method for machine learning power system stability assessment models is proposed based on Shapley value. Shapley values represent the contribution of each state variable to the stability evaluation result. By constructing kernel function and simplifying the form of training data, Shapley values are efficiently calculated based on the weighted linear regression. The resulting additive feature contribution model can be used to explain the internal logic of the prediction and verify the credibility of the model. The experiment results on voltage margin estimation model in the New England 39-bus system demonstrate the correctness and comprehensibility of the interpretations generated by the proposed method.