A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment

As one of the most complex and largest dynamic industrial systems, a modern power grid envisages the wide-area measurement protection and control (WAMPAC) system as the grid sensing backbone to enhance security, reliability, and resiliency. However, based on the massive wide-area measurement data, how to realize real-time short-term voltage stability (STVS) assessment is an essential yet challenging problem. This paper proposes a hierarchical and self-adaptive data-analytics method for real-time STVS assessment covering both the voltage instability and the fault-induced delayed voltage recovery phenomenon. Based on a strategically designed ensemble-based randomized learning model, the STVS assessment is achieved sequentially and self-adaptively. Besides, the assessment accuracy and the earliness are simultaneously optimized through the multiobjective programming. The proposed method has been tested on a benchmark power system, and its exceptional assessment accuracy, speed, and comprehensiveness are demonstrated by comparing with existing methods.Ministry of Education (MOE)Nanyang Technological UniversityThis work was supported in part by the ARC Discovery under Grant DP170103427, in part by the funding from Tyree foundation, in part by Singapore Ministry of Education under an Academic Research Fund Tier 1 project, and in part by National Natural Science Foundation of China (Project #51777173). The work of Y. Zhang is supported by the Research Training Program from Australian government. The work of Y. Xu is supported by Nanyang Assistant Professorship from Nanyang Technological University, Singapore. Paper no. TII-17-0906. (Corresponding author: Yan Xu.