This paper presents a novel optimal neurocontroller that replaces the conventional controller (CONVC), which consists of the automatic voltage regulator 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 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.
J. Park et al., "Adaptive-critic-Based Optimal Neurocontrol for Synchronous Generators in a Power System using MLP/RBF Neural Networks," IEEE Transactions on Industry Applications, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/TIA.2003.816493
Electrical and Computer Engineering
Keywords and Phrases
MLP/RBF Neural Networks; Adaptive-Critic-Based Optimal Neurocontrol; Dynamic Programming; Heuristic Dynamic Programming; Machine Control; Multilayer Perceptron Neural Network; Multilayer Perceptrons; Neurocontrollers; Optimal Control; Optimal Neurocontroller; Power System; Radial Basis Function Networks; Radial Basis Function Neural Network; Robustness; Synchronous Generators; System Damping; Transient Improvement
International Standard Serial Number (ISSN)
Article - Journal
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.