Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff

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 oper...

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Vydáno v:	IEEE transactions on industrial informatics Ročník 13; číslo 5; s. 2544 - 2554
Hlavní autoři:	Zhang, Yuchen, Xu, Yan, Dong, Zhao Yang, Xu, Zhao, Wong, Kit Po
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.10.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Computational modeling Decision making Dynamic insecurity risk early warning Early warning systems Electronic mail extreme learning machine (ELM) intelligent system (IS) Mathematical programming multiobjective programming (MOP) Multiple objective analysis Neural networks On-line systems Power sources Power system dynamics Power system stability Randomness Reliability Solar generators Tradeoffs Training
ISSN:	1551-3203, 1941-0050
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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.
AbstractList	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.
Author	Kit Po Wong Zhao Xu Zhao Yang Dong Yuchen Zhang Yan Xu
Author_xml	– sequence: 1 givenname: Yuchen surname: Zhang fullname: Zhang, Yuchen – sequence: 2 givenname: Yan surname: Xu fullname: Xu, Yan – sequence: 3 givenname: Zhao Yang surname: Dong fullname: Dong, Zhao Yang – sequence: 4 givenname: Zhao surname: Xu fullname: Xu, Zhao – sequence: 5 givenname: Kit Po surname: Wong fullname: Wong, Kit Po
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SubjectTerms	Accuracy Computational modeling Decision making Dynamic insecurity risk early warning Early warning systems Electronic mail extreme learning machine (ELM) intelligent system (IS) Mathematical programming multiobjective programming (MOP) Multiple objective analysis Neural networks On-line systems Power sources Power system dynamics Power system stability Randomness Reliability Solar generators Tradeoffs Training
Title	Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff
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