Critical zone identification framework for bulk electric system security assessment

Abstract

Climate change introduces a new set of vulnerabilities. It might cause unexpected voltage and frequency violation problems that can damage the existing power system configurations. Identifying the zones containing more critical violations in a bulk system under different climatic conditions and contingencies is crucial to securing the power system operation and infrastructure safety. This paper presents an advanced critical zone identification framework that can retrieve information from extensive data yielded by dynamic contingency analysis results from a large interconnected power grid. A machine learning (ML) based clustering approach is applied to identify the critical zone under hundreds of scenarios. The developed framework is tested using the 2028 western electricity coordinating council (WECC) system, and the detailed results are discussed in this paper.