Title :
Combined inverse system method and neural network for designing nonlinear excitation control law
Author :
Zhang, C.H. ; MacAlpine, J.M.K. ; Leung, T.P. ; Zhou, Q.J.
Author_Institution :
Dept. of Autom., South China Univ. of Technol., Guangzhou, China
Abstract :
In this paper, an inverse system method (ISM), which is a simple exact linearization method for nonlinear system design, has been employed to design a nonlinear excitation control law of synchronous generator, and neural network is proposed for the controller to provide desired controller output. The main motivation is to exploit generalization capabilities of neural networks to interpolate between training data, and thus to deal with system parametric uncertainties caused by a large sudden fault. Simulation results show that transient stability of the perturbed power system can be improved
Keywords :
control system synthesis; linearisation techniques; neurocontrollers; nonlinear control systems; power system control; power system stability; power system transients; synchronous generators; exact linearization method; generalization capabilities; inverse system method; large sudden fault; neural network; nonlinear excitation control law; nonlinear system design; parametric uncertainties; perturbed power system; synchronous generator; transient stability; Control systems; Neural networks; Nonlinear control systems; Nonlinear systems; Power system simulation; Power system stability; Power system transients; Synchronous generators; Training data; Uncertainty;
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
DOI :
10.1109/ICNN.1995.487501
