Novel Optimal Neurocontrol for a Synchronous Generator Using Radial Basis Function Neural Network
This paper presents the design of an infinite horizon optimal neurocontroller to replace the conventional controllers such as the automatic voltage regulator and governor for the control of a synchronous generator connected to an electric power grid. The neurocontroller design uses the dual heuristic programming (DHP) algorithm, which provides the most robust control capability among the adaptive critic designs (ACDs) family. The radial basis function neural network (RBFNN) is used as the function approximator to implement the DHP. The performances of the proposed optimal neurocontroller are evaluated and its stability issue in real-time operation is analyzed.
J. Park et al., "Novel Optimal Neurocontrol for a Synchronous Generator Using Radial Basis Function Neural Network," IFAC Symposium on Power Plants and Power Systems Control, Elsevier, Jan 2003.
Electrical and Computer Engineering
Keywords and Phrases
Dual Heuristic Programming; Optimal Control; Radial Basis Function Neural Network
Library of Congress Subject Headings
Article - Conference proceedings
© 2003 Elsevier, All rights reserved.