Nonlinear optimal control with effective wind speed estimation for maximum power extraction based on adaptive fuzzy logic controller and extended Kalman Filter

This paper aims to improve the efficiency of wind turbines by generating maximum power during partial load operation and under different wind speed characteristics. In most cases, the optimal control based on the maximum power point tracking (MPPT) of the wind turbine is used to optimize the effectiveness of the energy capture. In this study, the Tip Speed Ratio (TSR) method based on PI and adaptive fuzzy logic controllers is performed to apply the MPPT algorithm on a wind turbine based on a two-mass drive-train. Additionally, the effective wind speed is estimated by using an Extended Kalman Filter (EKF) based on Backward Euler Approximation (BEA) for continuous state space discretization. In fact, the controllers of the TSR method are tested based on the estimated wind speed obtained from the EKF output instead of the measurement obtained from the anemometer. This latter is due to the fact that the anemometer measurements represent the turbulent wind speed and cannot represent the wind speed upstream of the wind turbine rotor blades. In this paper, the efficiency of EKF is tested under different wind speed characteristics by varying the mean wind speed and the turbulence intensity. To compare the results obtained, the covariance and the correlation coefficient are measured each 5 ms to compute the similarity ratio between the measured and estimated wind speed. Then, the efficiency of TSR controllers on the wind turbine model based on a two-mass drive-train coupled with a common spring and damper is highlighted. It is found that the computed covariance and the correlation coefficient demonstrate that the EKF has high accuracy with a 98% similarity between the measured and estimated wind speeds. Further, the TSR based on adaptive fuzzy logic and PI controllers has an effectiveness to improve the efficiency of wind turbines by extracting maximum power from the wind energy. However, the comparison results illustrate that the TSR based on adaptive fuzzy logic controller can capture slightly more wind energy than the other one under different wind speed characteristics.