Physics-Augmented Auxiliary Learning for Power System Transient Stability Assessment

Data-driven transient stability assessment (TSA) methods have proven effectiveness in addressing the security challenges posed by the rapid integration of renewable energy into power systems. However, these methods often face high training costs, physical inconsistency, and limited generalization. To address these limitations, this article proposes a physics-augmented auxiliary learning (PA-AL) framework for TSA, implemented in a multigate mixture of expert model and a physics-informed paradigm for end-to-end stability margin prediction. Unlike conventional physics-informed neural network-based approaches, which rely on rotor angle trajectory predictions prone to cumulative errors, PA-AL embedding physics law by integrating auxiliary electrical velocity predictions. The proposed PA-AL framework employs a dual-phase physics-guided training scheme: first, dynamic-guided state representation learning, which captures system states by minimizing a loss function with auxiliary empirical terms and physics regularization derived from discrete swing equations, and second, mechanism-guided margin estimation calibration, which refines stability margin predictions through a mechanism-enhanced regularization term leveraging the relationship between margin and velocity deviation. Extensive case studies on the WSCC 9-bus, New England 39-bus, IEEE 68-bus, and Iceland 189-bus systems demonstrate the effectiveness of the proposed approach in improving stability assessment accuracy and maintaining physics consistency.