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

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

doi:10.1109/TIA.2003.816493

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

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

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

75 Citations (Scopus)

Abstract

This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator 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 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)	1529-1540
Number of pages	12
Journal	IEEE Transactions on Industry Applications
Volume	39
Issue number	5
DOIs	https://doi.org/10.1109/TIA.2003.816493
Publication status	Published - 2003 Sept

Bibliographical note

Funding Information:
Paper MSDAD-A 03–07, presented at the 2002 Industry Applications Society Annual Meeting, Pittsburgh, PA, October 13–18, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Industrial Automation and Control Committee of the IEEE Industry Applications Society. Manuscript submitted for review October 15, 2002 and released for publication June 2, 2003. This work was supported by the National Science Foundation under Grant ECS-0080764.

All Science Journal Classification (ASJC) codes

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

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title = "Adaptive-critic-based optimal neurocontrol for synchronous generators in a 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 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 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 Kumar}",

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

Abstract

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