Visible to the public An Evolutionary Computation Approach for Smart Grid Cascading Failure Vulnerability Analysis

TitleAn Evolutionary Computation Approach for Smart Grid Cascading Failure Vulnerability Analysis
Publication TypeConference Paper
Year of Publication2019
AuthorsJiang, He, Wang, Zhenhua, He, Haibo
Conference Name2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywordsbenchmark systems, Cascading Failures, composability, Cyber-physical security, evolutionary computation, evolutionary computation based vulnerability analysis framework, failure analysis, learning (artificial intelligence), line-switching, line-switching sequential attack, Metrics, particle swarm optimisation, particle swarm optimization, power engineering computing, Power Grid Vulnerability Assessment, power system faults, Power system protection, power system security, power transmission lines, power transmission protection, power transmission reliability, pubcrawl, reinforcement learning, resilience, Resiliency, security of data, smart grid cascading failure vulnerability analysis, smart grid protection, smart grid security, Smart grids, smart power grids, Topology
AbstractThe cyber-physical security of smart grid is of great importance since it directly concerns the normal operating of a system. Recently, researchers found that organized sequential attacks can incur large-scale cascading failure to the smart grid. In this paper, we focus on the line-switching sequential attack, where the attacker aims to trip transmission lines in a designed order to cause significant system failures. Our objective is to identify the critical line-switching attack sequence, which can be instructional for the protection of smart grid. For this purpose, we develop an evolutionary computation based vulnerability analysis framework, which employs particle swarm optimization to search the critical attack sequence. Simulation studies on two benchmark systems, i.e., IEEE 24 bus reliability test system and Washington 30 bus dynamic test system, are implemented to evaluate the performance of our proposed method. Simulation results show that our method can yield a better performance comparing with the reinforcement learning based approach proposed in other prior work.
Citation Keyfei_reserch_2019