Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

In recent years the use of renewable energy including wind energy has risen dramatically. Because of the increasing development of wind power production, improvement of the control of wind turbines using classical or intelligent methods is necessary. To optimize the power produced in a wind turbine, the speed of the turbine should vary with the wind speed. Variable-speed operation of wind turbines presents certain advantages over constant-speed operation. In this paper, in order to maintain the maximal output power of wind turbine, a novel intelligent controller based on the adaptive neuro-fuzzy inference system (ANFIS) is designed. To improve the wind energy available in an erratic wind speed regime, a wind generator equipped with continuously variable transmission (CVT) was proposed. In this model the ANFIS regulator adjusts the system speed, i.e. CVT ratio, for operating at the highest efficiency point. The performance of proposed controller is confirmed by simulation results. Some outstanding properties of this new controller are online implementation capability, structural simplicity and its robustness against any changes in wind speed and system parameter variations. Based on the simulation results, the effectiveness of the proposed controllers was verified. (C) 2013 Elsevier Ltd. All rights reserved.