Adaptive-Neural Pid Control of Wind Energy Conversion Systems Using Wavenets

In this paper a PID control strategy using neural network adaptive RASP1 wavelet for WECS´s control is proposed. It is based on 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 (WECS´s) and generate the control signal. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution