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

Research output: Contribution to journalConference 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 languageEnglish
Pages (from-to)1447-1454
Number of pages8
JournalConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Volume2
Publication statusPublished - 2002 Jan 1
Event37th IAS Annual Meeting and World Conference on Industrial applications of Electrical Energy - Pittsburgh, PA, United States
Duration: 2002 Oct 132002 Oct 18

All Science Journal Classification (ASJC) codes

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

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