Neuro-fuzzy approach to real-time transient stability prediction based on synchronized phasor measurements

Chih Wen Liu, Shuenn Shing Tsay, Yi Jen Wang, Mu Chun Su

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

With new systems capable of making synchronized phasor measurements there are possibilities for real-time assessment of the stability of a transient swing in power systems. In the future, on-line control will be necessary as operating points are pushed closer toward the margin and fast reaction time becomes critical to the survival of the system. In this paper we develop a novel class of fuzzy hyperrectangular composite neural networks which utilize real-time phasor angle measurements to provide fast transient stability prediction for use with high-speed control. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.

Original languageEnglish
Pages (from-to)123-127
Number of pages5
JournalElectric Power Systems Research
Volume49
Issue number2
DOIs
StatePublished - 1 Mar 1999

Keywords

  • Fuzzy hyperrectangular composite neural network (FHRCNN)
  • Phasor measurement unit (PMU)
  • Real-time transient stability prediction

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