SEDIGHIZADEH - PhD Student Power System Studies Dept.-Power Engineering Consultants-MOSHANIR-Tehran-Iran
KALANTAR - Associate professor Electrical Dept.-Iran University of Science and Technology-Narmak-Tehran- ۱۶۸۴۴,Iran

Grid connected wind energy conversion systems (WECS) present interesting control demands, due to the intrinsic nonlinear characteristics of windmills and electric generators. In this paper a PID control strategy using neural network adaptive RASP1 wavelet for WECS control is proposed. It is based on a single layer feedforward neural networks with hidden nodes of adaptive RASP1 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 PID controller assumes a certain model structure to approximately identify the system dynamics of the unknown plant and generate the control signal. The capability of neuro PID controller to self tuning of an unknown plant is then illustrated through WECS. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Adaptive PID Control, RASP1 Wavelets, Wind Energy Conversion System