Dual heuristic programming based nonlinear optimal control for a synchronous generator

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

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper presents the design of an infinite horizon nonlinear optimal neurocontroller that replaces the conventional automatic voltage regulator and the turbine governor (CONVC) for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the novel optimization neuro-dynamic programming algorithm based on dual heuristic programming (DHP), which has the most robust control capability among the adaptive critic designs family. The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP technique. The DHP based optimal neurocontroller (DHPNC) using the RBFNN shows improved dynamic damping compared to the CONVC even when a power system stabilizer is added. Also, the DHPNC provides a robust feedback loop in real-time operation without the need for continual on-line training, thus reducing any risk of possible instability associated with the neural network based controllers.

Original languageEnglish
Pages (from-to)97-105
Number of pages9
JournalEngineering Applications of Artificial Intelligence
Volume21
Issue number1
DOIs
Publication statusPublished - 2008 Feb 1

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Keywords

  • Adaptive critic designs
  • Dual heuristic programming
  • Optimal control
  • Power system stabilizer
  • Radial basis function neural network
  • Synchronous generator

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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