A New Neural Network Paradigm for Scalable and Generalizable Stability Analysis of Power Systems

Abstract

This paper presents a new neural network (NN) paradigm for scalable and generalizable stability analysis of power systems. The paradigm consists of two parts: the neural stability descriptor and the sample-augmented iterative training scheme. The first part, based on system decomposition, constructs the object (such as a stability function or condition) for stability analysis as a scalable aggregation of multiple NNs. These NNs remain fixed across varying power system structures and parameters, and are repeatedly shared within each system instance defined by these variations, thereby enabling the generalization of the neural stability descriptor across a class of power systems. The second part learns the neural stability descriptor by iteratively training the NNs with sample augmentation, guided by the tailored conservativeness-aware loss function. The training set is strategically constructed to promote the descriptor's generalizability, which is systematically evaluated by verification and validation during the training process. Specifically, the proposed NN paradigm is implemented for large-disturbance stability analysis of the bulk power grid and small-disturbance stability conditions of the microgrid system. Finally, numerical studies for the two implementations demonstrate the applicability and effectiveness of the proposed NN paradigm.