Adaptive critic based optimal neurocontrol for synchronous generator in power system using MLP/RBF neural networks

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

Adaptive critic based optimal neurocontrol for synchronous generator in power system using MLP/RBF neural networks

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Conference article

7 Citations (Scopus)

Abstract

This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator (AVR) and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design (ACD) technique is used for the design of the neurocontroller. The performance of the MLPN based HDP neurocontroller (MHDPC) is compared with the RBFN based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared with the CONVC. Simulation results are presented to show that the proposed neurocontrollers provide stable convergence with robustness, and the RHDPC outperforms the MHDPC and CONVC in terms of system damping and transient improvement.

Original language	English
Pages (from-to)	1447-1454
Number of pages	8
Journal	Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Volume	2
Publication status	Published - 2002 Jan 1
Event	37th IAS Annual Meeting and World Conference on Industrial applications of Electrical Energy - Pittsburgh, PA, United States Duration: 2002 Oct 13 → 2002 Oct 18

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

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Park, J. W., Harley, R. G., & Venayagamoorthy, G. K. (2002). Adaptive critic based optimal neurocontrol for synchronous generator in power system using MLP/RBF neural networks. Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), 2, 1447-1454.

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title = "Adaptive critic based optimal neurocontrol for synchronous generator in power system using MLP/RBF neural networks",

abstract = "This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator (AVR) and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design (ACD) technique is used for the design of the neurocontroller. The performance of the MLPN based HDP neurocontroller (MHDPC) is compared with the RBFN based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared with the CONVC. Simulation results are presented to show that the proposed neurocontrollers provide stable convergence with robustness, and the RHDPC outperforms the MHDPC and CONVC in terms of system damping and transient improvement.",

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

year = "2002",

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language = "English",

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note = "37th IAS Annual Meeting and World Conference on Industrial applications of Electrical Energy ; Conference date: 13-10-2002 Through 18-10-2002",

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T1 - Adaptive critic based optimal neurocontrol for synchronous generator in power system using MLP/RBF neural networks

AU - Park, Jung Wook

AU - Harley, Ronald G.

AU - Venayagamoorthy, Ganesh K.

PY - 2002/1/1

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N2 - This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator (AVR) and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design (ACD) technique is used for the design of the neurocontroller. The performance of the MLPN based HDP neurocontroller (MHDPC) is compared with the RBFN based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared with the CONVC. Simulation results are presented to show that the proposed neurocontrollers provide stable convergence with robustness, and the RHDPC outperforms the MHDPC and CONVC in terms of system damping and transient improvement.

AB - This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator (AVR) and turbine governor, to control a synchronous generator in a power system using a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN). The heuristic dynamic programming (HDP) based on the adaptive critic design (ACD) technique is used for the design of the neurocontroller. The performance of the MLPN based HDP neurocontroller (MHDPC) is compared with the RBFN based HDP neurocontroller (RHDPC) for small as well as large disturbances to a power system, and they are in turn compared with the CONVC. Simulation results are presented to show that the proposed neurocontrollers provide stable convergence with robustness, and the RHDPC outperforms the MHDPC and CONVC in terms of system damping and transient improvement.

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