Title :
Parameter Identification of Excitation Systems Based on Hopfield Neural Network
Author :
Liao, Q.F. ; Liu, D.C. ; Ying, L.M. ; Cui, X. ; Li, Y. ; He, W.T.
Author_Institution :
Sch. of Electr. Eng., Wuhan Univ., Wuhan
Abstract :
The parameter identification based on Hopfield neural network (HNN) was applied to a static excitation system. The applicable algorithm of the identification method was given in detail. Nine-parameter excitation system was studied. The HNN of twenty neurons were designed in order to identify these parameters. Finally model validation was performed. Numerical simulation results testify that this method has high precision and quick convergence. The method can be implemented with electronic circuit, so it will benefit the on-line parameter identification of the excitation system and will have significance to any system that can be described by state space model.
Keywords :
Hopfield neural nets; power engineering computing; power system parameter estimation; state-space methods; Hopfield neural network; electronic circuit; parameter identification method; state space model; static excitation system; Circuit testing; Hopfield neural networks; Nonlinear dynamical systems; Parameter estimation; Power system control; Power system dynamics; Power system modeling; Power systems; State-space methods; System testing; Excitation system; Hopfield neural network (HNN); Parameter estimation; State space model; System identification;
Conference_Titel :
Power System Technology, 2006. PowerCon 2006. International Conference on
Conference_Location :
Chongqing
Print_ISBN :
1-4244-0110-0
Electronic_ISBN :
1-4244-0111-9
DOI :
10.1109/ICPST.2006.321809
