Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74727
Title: Intelligent early warning of power system dynamic insecurity risk : toward optimal accuracy-earliness tradeoff
Authors: Zhang, Y
Xu, Y
Dong, ZY
Xu, Z 
Wong, KP
Keywords: Dynamic insecurity risk
Early warning
Extreme learning machine (ELM)
Intelligent system (IS)
Multiobjective programming (MOP)
Issue Date: 2017
Publisher: IEEE Computer Society
Source: IEEE transactions on industrial informatics, 2017, v. 13, no. 5, 7869388, p. 2544-2554 How to cite?
Journal: IEEE transactions on industrial informatics 
Abstract: Dynamic insecurity risk of a power system has been increasingly concerned due to the integration of stochastic renewable power sources (such as wind and solar power) and complicated demand response. In this paper, an intelligent early-warning system to achieve reliable online detection of risky operating conditions is proposed. The proposed intelligent system (IS) consists of an ensemble learning model based on extreme learning machine (ELM) and a decision-making process under a multiobjective programming framework. Taking an ensemble form, the randomness existing in individual ELM training is generalized and reliable classification results can be obtained. The decision making is designed for ELM ensemble whose parameters are optimized to search for the optimal tradeoff between the warning accuracy and the warning earliness of the proposed IS. The compromise solution turns out to significantly speed up the overall computation with an acceptable sacrifice in the accuracy (e.g., from 100% to 99.9%). More importantly, the proposed IS can provide multiple and switchable performances to the operators in order to satisfy different local dynamic security assessment requirements.
URI: http://hdl.handle.net/10397/74727
ISSN: 1551-3203
EISSN: 1941-0050
DOI: 10.1109/TII.2017.2676879
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