Conceptual designs of AI-based systems for local prediction of voltage collapse

K. Yabe, J. Koda, K. Yoshida, K. H. Chiang, P. S. Khedkar, D. J. Leonard, N. W. Miller

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Vulnerability of modern power systems to locally initiated voltage collapse gives rise to a need for methods to measure local voltage security and to predict voltage instability. The paper presents a novel architecture based on a suite of AI technologies and three-dimensional PQV surfaces which provides prediction of local voltage collapse and indices of system voltage security. Robustness and adaptation are demonstrated on difficult and realistic power system simulation models.

Original languageEnglish
Pages (from-to)137-145
Number of pages9
JournalIEEE Transactions on Power Systems
Volume11
Issue number1
DOIs
Publication statusPublished - 1996
Externally publishedYes

Keywords

  • Artificial intelligence
  • Fuzzy kaiman filters
  • Neuro-fuzzy systems
  • Voltage stability

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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