GRNN-Based Real-Time Fault Chain Prediction

نویسندگان

چکیده

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying FCs is pivotal to alleviating failures, complex spatio-temporal dependencies among components power system render challenges modeling and analyzing FCs. Furthermore, faces an inherent combinatorial complexity that grows exponentially with size system. The proposed leverages recent advances in graph recurrent neural networks circumvent computational complexities process formalized as partially observable Markov decision (POMDP), which subsequently solved via time-varying network (GRNN) judiciously accounts temporal spatial structures data generated by key features this structure include (i) leveraging induced topology, (ii) dynamics, (iii) efficiently summarizing system's history latent space GRNN. framework's efficiency compared relevant literature on IEEE 39-bus New England 118-bus

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ژورنال

عنوان ژورنال: IEEE Transactions on Power Systems

سال: 2023

ISSN: ['0885-8950', '1558-0679']

DOI: https://doi.org/10.1109/tpwrs.2023.3258740