Fuzzy logic based automatic capacitor switching for reactive power compensation

A. T.P. So, W. L. Chan, C. T. Tse

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

To cope with the growth of load demand, it is known that one of the power utilities in Hong Kong tried to implement an automatic scheme for capacitor switching. However, due to the frequent switching which occurred, resulting in hunting, and the difficulty in determining suitable ON/OFF settings, the scheme was unsuccessful. The authors propose a pragmatic design of a capacitor switching controller which has been successfully tested. Besides power factor, the control signal also incorporates MVAr and the advantages are highlighted. To improve the reliability and robustness of the system, fuzzy logic has been introduced. The performance is described based on both the conventional step control and the continuous TCR/TSC (thyristor controlled reactor/thyristor switched capacitor) control.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993
EditorsY. Tamura, H. Suzuki, H. Mori
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-46
Number of pages6
ISBN (Electronic)0780312171, 9780780312173
DOIs
Publication statusPublished - Apr 1993
Event2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993 - Yokohama, Japan
Duration: 19 Apr 199322 Apr 1993

Publication series

NameProceedings of the 2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993

Conference

Conference2nd International Forum on Applications of Neural Networks to Power Systems, ANNPS 1993
Country/TerritoryJapan
CityYokohama
Period19/04/9322/04/93

Keywords

  • Fuzzy Control
  • Optimization & Control of Power System

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Control and Optimization
  • Modelling and Simulation
  • Artificial Intelligence

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