Adaptive Stability Contingency Screening for Operational Planning Based on Domain-Adversarial Graph Neural Network

Compared to contingency screening (CS) techniques that focus on thermal/voltage limit violation, CS that addresses the transient and small-signal stability contingencies in the existing references requires a higher computational cost, limiting its use to off-line contingency analysis. Therefore, it poses the challenge of developing an effective CS scheme for fast recognition of critical contingency, which is one of the major concerns of system operators. To deal with this problem, this article proposes an adaptive stability CS (SCS) scheme for operational planning. One challenge in developing the adaptive SCS for different cardinal points is data distribution discrepancy resulted from load/topology changes. To align data distribution between different domains (i.e., cardinal points), a domain-adversarial graph neural network (DAGNN) is developed to learn the domain-invariant features, so that the DAGNN model trained on the labeled source domain (e.g., peak cardinal point 2B) data can be applied to the unlabeled target domain (e.g., trough cardinal point 1B) data for SCS. To make the proposed SCS more efficient in dealing with power system data, a graph learning approach combined with graph transformer and graph isomorphism network is used in DAGNN to provide feature representations considering the graph properties of power systems, where nodes and edges refer to buses and transmission lines, respectively. Experiments on IEEE 39 Bus system and IEEE 118 Bus system have verified the effectiveness of the proposed model.