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

• Main Authors: Zhang, Yuchen, Xu, Yan, Dong, Zhao Yang, Zhang, Rui
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Data-analytics; Ensemble Learning
• Online Access: https://hdl.handle.net/10356/151001
• Institution: Nanyang Technological University

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.