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

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

doi:10.1016/S14746670(17)34448-8

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

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Pages (from-to)	85-90
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	36
Issue number	20
DOIs	https://doi.org/10.1016/S14746670(17)34448-8
Publication status	Published - 2003
Event	5th IFAC Symposium on Power Plants and Power Systems Control 2003 - Seoul, Korea, Republic of Duration: 2003 Sept 15 → 2003 Sept 19

Bibliographical note

Publisher Copyright:
Copyright © 2003 IFAC.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

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10.1016/S14746670(17)34448-8

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title = "Novel optimal neurocontrol for a synchronous generator using radial basis function neural network",

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.",

author = "Park, {Jung Wook} and Harley, {Ronald G.} and Venayagamoorthy, {Ganesh K.}",

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AU - Park, Jung Wook

AU - Harley, Ronald G.

AU - Venayagamoorthy, Ganesh K.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

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DO - 10.1016/S14746670(17)34448-8

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SN - 2405-8963

VL - 36

SP - 85

EP - 90

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 20

T2 - 5th IFAC Symposium on Power Plants and Power Systems Control 2003

Y2 - 15 September 2003 through 19 September 2003

ER -

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

Abstract

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