A Hierarchical Self-Adaptive Data-Analytics Method for Real-Time Power System Short-Term Voltage Stability Assessment

• Published in: IEEE transactions on industrial informatics 2019-01, Vol.15 (1), p.74-84
• Main Authors: Zhang, Yuchen, Xu, Yan, Dong, Zhao Yang, Zhang, Rui
• Format: Article
• Language: English
• Subjects: Adaptive systems; Analytics; Data analysis; Data-analytics; Dynamic stability; Electric potential; ensemble learning; extreme learning machine; Indexes; Mathematical programming; multiobjective programming; Multiple objective analysis; Power system dynamics; Power system stability; Real time; Real-time systems; Reliability; short-term voltage stability (STVS); smart grid; Stability; Stability analysis; Stability criteria; Voltage control; Voltage stability
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2018.2829818

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.