Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine

Abstract

The precise measurement of effective wind speed is a crucial task and has huge impact on wind turbine output power, safety and control performance. In this study, a hybrid intelligent learning based adaptive neuro-fuzzy inference system (ANFIS) is proposed for online estimation of effective wind speed from instantaneous values of wind turbine tip speed ratio (TSR), rotor speed and mechanical power. The artificial neural network (ANN) adjusts the parameters of fuzzy membership functions (MFs) using hybrid optimization method. The estimated value of effective wind speed is further utilized to design the optimal rotor speed estimator for maximum power point tracking (MPPT) of variable-speed wind turbine (VSWT). Both estimators are implemented in MATLAB and their performance is investigated for national renewable energy laboratory (NREL) offshore 5 MW baseline wind turbine. The simulation results show the effectiveness of proposed method. The proposed scheme is computationally intelligent, easy to implement and more reliable for fast estimation of effective wind speed and optimal rotor speed.