Novel optimal neurocontrol for a synchronous generator using radial basis function neural network

Jung Wook Park, Ronald G. Harley, Ganesh K. Venayagamoorthy

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

This paper presents the design of an infinite horizon optimal neurocontroller to replace the conventional controllers such as the automatic voltage regulator and governor for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the dual heuristic programming (DHP) algorithm, which provides the most robust control capability among the adaptive critic designs (ACDs) family. The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP. The perfonnances of the proposed optimal neurocontroller are evaluated and its stability issue in real-time operation is analysed.

Original languageEnglish
Pages (from-to)85-90
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume36
Issue number20
DOIs
Publication statusPublished - 2003
Event5th IFAC Symposium on Power Plants and Power Systems Control 2003 - Seoul, Korea, Republic of
Duration: 2003 Sep 152003 Sep 19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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