Title :
Adaptive PID control of wind energy conversion systems using wavenets
Author :
Sedighizadeh, M. ; Kalantar, M.
Author_Institution :
Dept. of Eng., Azad Univ. of Saveh, Iran
Abstract :
In this paper a PID control strategy using neural network adaptive RASP1 wavelet for WECS control is proposed. It is based on single layer feedforward neural networks with hidden nodes of adaptive RASP1 wavelet function controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide a double local structure resulting in improving the speed of learning. This particular neuro PID controller assumes a certain model structure to approximately identify the system dynamics of the unknown plant (WECS) and generate the control signal. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.
Keywords :
IIR filters; adaptive control; electric generators; feedforward neural nets; learning (artificial intelligence); neurocontrollers; power system control; recurrent neural nets; three-term control; wavelet transforms; wind power plants; wind turbines; IIR recurrent structure; WECS control; adaptive PID control; adaptive RASP1 wavelet; controller; double local structure; hidden nodes; infinite impulse response; learning speed; neurocontroller; single layer feedforward neural networks; system dynamics; turbine/generator pair; unknown plant; wavenets; wind energy conversion systems; Adaptive control; Adaptive systems; Control systems; Feedforward neural networks; Neural networks; Programmable control; Recurrent neural networks; Signal generators; Three-term control; Wind energy;
Conference_Titel :
Universities Power Engineering Conference, 2004. UPEC 2004. 39th International
Print_ISBN :
1-86043-365-0
