Dual Heuristic Programming Based Nonlinear Optimal Control for a Synchronous Generator

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.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

Ministry of Commerce, Industry and Energy

Keywords and Phrases

Adaptive Critic Designs; Dual Heuristic Programming; Optimal Control; Power System Stabilizer; Radial Basis Function Neural Network; Synchronous Generator

International Standard Serial Number (ISSN)

0952-1976

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 Elsevier, All rights reserved.

Publication Date

01 Feb 2008

Link to Full Text

Share

 
COinS