Enhancing Trustworthiness of Data-Driven Power System Dynamic Security Assessment via Hybrid Credibility Learning

This letter proposed a hybrid credibility learning method for data-driven Dynamic Security Assessment (DSA), aiming to enhance the trustworthiness of DSA results under out-of-distribution changing system conditions. The proposed method integrates multiple credibility criteria as learning inputs, combining model consistency—reflected through the output distribution of ensemble learners—with novel indicators of data disparities, namely the Degree of Outlier (DOO) and One-sample Maximum Mean Discrepancy (O-MMD). Moreover, the credibility model benefits from training on a specifically designed out-of-distribution dataset which is further reinforced through misclassified DSA instances. This letter also provides a comprehensive comparison of existing credible DSA methods via the proposed trustworthiness evaluation index. Simulation results on the benchmark testing system show that the proposed method can achieve the best trade-off between DSA accuracy and credibility under various extreme system conditions.