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.