AUTHOR=Huang Lingxiang , Dong Kun , Zhao Jianfeng , Liu Kangli , Jin Cheng , Guo Xirui 

TITLE=A multi-task transient stability assessment method adapted to topology changes using multi-graph sample and aggregate-attention network

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1321998

DOI=10.3389/fenrg.2023.1321998

ISSN=2296-598X

ABSTRACT=Transient stability assessment (TSA) plays a pivotal role in guiding power grid risk control strategies. However, it faces challenges when dealing with complex multi-graph inputs generated by pre-fault, fault occurrence, and post-fault states. Meanwhile, most previous researches neglected the assessment of the transient stability level. To address this, we propose a multi-task transient stability assessment (MTTSA) approach. In MTTSA, we introduce a multi-graph sample and aggregateattention network (GraphSAGE-A) designed to capture stability features even amidst topology changes. A multi-head attention mechanism and a local normalization method are adopted for a better extraction of the global and contextual information. Additionally, we introduce a quantified Transient stability risk index (RI) considering transient stability boundary (TSB) as well as incorporate a multitask dense structure to enhance MTTSA's performance. Empirical tests under changing operating conditions conducted on the IEEE 39 Bus system showcase a significant performance improvement with the proposed MTTSA method. * , θ jv * , P jv * , Q jv * ) ∈ V Kj (7) U jv * , θ jv * , P jv * and Q jv * are elements of node features V Kj . Here we adopt local normalization of the adjacency matrix to meet the requirements of local aggregation as needed by the GraphSAGE algorithm. The method is: