KALMAN FILTER AND DEEP REINFORCEMENT LEARNING BASED WIND TURBINE YAW MISALIGNMENT CONTROL METHOD

摘要

A Kalman filter and deep reinforcement learning based yaw misalignment control method of wind turbines is disclosed. During the normal operation of a wind turbine, the yaw misalignment control method of the present invention calculates non-stationary assembly angles by applying a Kalman filter to a series of really measured relative wind direction values and predicts non-stationary flow deflection angles through an actor-critic flow deflection angle prediction deep reinforcement learning model and then, by estimating and calibrating the yaw misalignment, the calibration of the yaw misalignment with the non-stationarity is completely automated, maximizing the reduction in operating costs for manual calibration of the non-stationarity of the yaw misalignment.