Visible to the public Sequential Node/Link Recovery Strategy of Power Grids Based on Q-Learning Approach

TitleSequential Node/Link Recovery Strategy of Power Grids Based on Q-Learning Approach
Publication TypeConference Paper
Year of Publication2019
AuthorsZhang, Yihan, Wu, Jiajing, Chen, Zhenhao, Huang, Yuxuan, Zheng, Zibin
Conference Name2019 IEEE International Symposium on Circuits and Systems (ISCAS)
Date Publishedmay
ISBN Number978-1-7281-0397-6
KeywordsCascading Failures, Cyber Attacks, grid defender, large-scale smart grids, learning (artificial intelligence), Load flow, physical attacks, power engineering computing, power flow cascading failure, power generation, power grid, power grid test benchmarks, power grids, power system faults, Power system protection, power system reliability, power system restoration, pubcrawl, q-learning, Q-learning approach, recovery strategies, reinforcement learning, resilience, Resiliency, restoration process, security of data, sequential node-link recovery strategy, sequential recovery, Smart grids, smart power grids, System recovery

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.

Citation Keyzhang_sequential_2019