摘要
Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.
基金
supported in part by the Guizhou Provincial Science and Technology Projects(ZK[2022]149)
the Special Foundation of Guizhou University(GZU)([2021]47)
the Guizhou Provincial Research Project for Universities([2022]104)
the GZU cultivation project of the National Natural Science Foundation of China([2020]80)
Shanghai Engineering Research Center of Big Data Management。
