Title :
Nonlinear model identification and control of wind turbine using wavenets
Author :
Sedighizadeh, M. ; Kalantar, M. ; Esfandeh, S. ; Arzaghi-Harris, D.
Author_Institution :
Dept. of Eng., Saveh Islamic Azad Univ.
Abstract :
In this paper a PI control strategy using neural network adaptive RASP1 wavelet for WECS´s control is proposed. It is based on single layer feed forward neural networks with hidden nodes of adaptive RASPl (rational functions with second-order) wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. This particular neuro PI controller assumes a certain model structure to approximately identify the system dynamics of the unknown plant (WECSs) and generate the control signal. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution
Keywords :
PI control; feedforward neural nets; identification; neurocontrollers; nonlinear control systems; power generation control; rational functions; wavelet transforms; wind turbines; IIR recurrent structure; PI control; WECS control; infinite impulse response; neural network adaptive RASP1 wavelet; neuro PI controller; nonlinear model identification; rational functions with second-order wavelet functions controller; single layer feed forward neural networks; wavenets; wind turbine control; Adaptive control; Adaptive systems; Feedforward neural networks; Feeds; Neural networks; Pi control; Programmable control; Recurrent neural networks; Signal generators; Wind turbines;
Conference_Titel :
Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-9354-6
DOI :
10.1109/CCA.2005.1507270
