Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151001
Title: A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment
Authors: Zhang, Yuchen
Xu, Yan
Dong, Zhao Yang
Zhang, Rui
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, Y., Xu, Y., Dong, Z. Y. & Zhang, R. (2018). A hierarchical self-adaptive data-analytics method for real-time power system short-term voltage stability assessment. IEEE Transactions On Industrial Informatics, 15(1), 74-84. https://dx.doi.org/10.1109/TII.2018.2829818
Journal: IEEE Transactions on Industrial Informatics 
Abstract: 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.
URI: https://hdl.handle.net/10356/151001
ISSN: 1551-3203
DOI: 10.1109/TII.2018.2829818
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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