Power-System Transient Stability Assessment Based on High-Level Sample Feature Extraction and Model Updating

To address the insufficient feature representation of conventional data-driven transient stability assessment (TSA) models and their limited adaptability to changes in power-system operating conditions, which result in inadequate assessment accuracy, this paper proposes a TSA method based on high-level sample feature extraction and model updating. First, a self-supervised contrastive random feature perturbation model for transient stability assessment, called TSA-SCRF, is developed. By introducing random perturbations into steady-state power flow features and employing contrastive learning, the proposed model extracts robust deep feature representations while preserving fault-type information. Second, a boundary-aware ensemble support vector machine (BAESVM) is constructed, which exploits multiple kernel functions to learn complementary discriminative information and dynamically assigns classifier weights according to both classifier performance and the samples’ decision distances. Finally, high-value newly acquired samples are selected based on sample uncertainty and, together with the support vectors of the original model, are utilized for model updating. Case studies conducted on a provincial power grid in China demonstrate that the proposed method improves the accuracy of data-driven transient stability assessment and enhances the model’s adaptability to changes in power-system operating conditions.