Intelligent time-adaptive transient stability assessment system

James J.Q. Yu, David J. Hill, Albert Y.S. Lam, Jiatao Gu, Victor O.K. Li

Research output: Contribution to journalArticleResearchpeer-review

259 Citations (Scopus)

Abstract

Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system.

Original languageEnglish
Pages (from-to)1049-1058
Number of pages10
JournalIEEE Transactions on Power Systems
Volume33
Issue number1
DOIs
Publication statusPublished - Jan 2018
Externally publishedYes

Keywords

  • Long short-term memory
  • Phasor measurement units
  • Recurrent neural network
  • Transient stability assessment
  • Voltage phasor.

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