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

Yuchen Zhang, Yan Xu, Zhao Yang Dong, Zhao Xu, Kit Po Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)

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.
Original languageEnglish
Article number7869388
Pages (from-to)2544-2554
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume13
Issue number5
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • Dynamic insecurity risk
  • early warning
  • extreme learning machine (ELM)
  • intelligent system (IS)
  • multiobjective programming (MOP)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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