High Order Recurrent Neural Control for Wind Turbine with a Permanent Magnet Synchronous Generator

In this paper, an adaptive recurrent neural control scheme is applied to a wind turbine with permanent magnet synchronous generator. Due to the variable behavior of wind currents, the angular speed of the generator is required at a given value in order to extract the maximum available power. In order to develop this control structure, a high order recurrent neural network is used to model the turbine-generator model which is assumed as an unknown system; a learning law is obtained using the Lyapunov methodology. Then a control law, which stabilizes the reference tracking error dynamics, is developed using Control Lyapunov Functions. Via simulations, the control scheme is applied to maximum power operating point on a small wind turbine.