A topology adaptive high-speed transient stability assessment scheme based on multi-graph attention network with residual structure.

• A scheme develops TSA almost immediately after fault occurrence. • A physical-enhanced graph attention mechanism with residual structure for topology learning. • A piece-wise stability index (PSI) is proposed. • Both the stability category and stability level are predicted. • Excellent accuracy and robustness to noise and topological changes. Reliable and fast transient stability assessment (TSA) is significantly required for the power system emergency control. We propose a topology adaptive high-speed transient stability assessment (HSTSA) scheme, where the inputs of the model adopt only the pre-fault state and the dynamics at the fault occurrence snapshot. A novel multi-graph attention network with residual structure (ResGAT) is designed to capture the stability characteristics. ResGAT applies improved graph attention mechanism to enhance its adaptability to the power system topology changes and the residual structure helps to avoid network degeneration. Meanwhile, a new piece-wise transient stability index (PSI) is proposed for the stability level prediction. Integration of both the stability category and the stability level results increase the precision of the HSTSA scheme. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models and its robustness under various scenarios.. [ABSTRACT FROM AUTHOR]