Dual Heuristic Programming Based Nonlinear Optimal Control for a Synchronous Generator
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.
J. Park et al., "Dual Heuristic Programming Based Nonlinear Optimal Control for a Synchronous Generator," Engineering Applications of Artificial Intelligence, Elsevier, Feb 2008.
The definitive version is available at https://doi.org/10.1016/j.engappai.2007.03.001
Electrical and Computer Engineering
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)
Article - Journal
© 2008 Elsevier, All rights reserved.