Fuzzy inference model synthesis of a novel magnetically levitated VAWT with MPPT via guide flaps

This paper presents a Fuzzy inference system (FIS) for levitated Vertical Axis Wind Turbine (VAWT) with Omni-directional guide vanes; which act as wind concentrator as well as wind shield. The non-linearities of Wind Energy Conversion System (WECS) are successfully mapped using fuzzy logic. Features of the proposed WECS include three concave rotating wind vanes situated at the external periphery of the turbine and a hybrid design of VAWT resulting in optimum tip speed ratio (TSR). Venturi effect is produced by dynamically altering the angles of the flaps by closed loop mechanism. The vanes shield the turbine from high velocity winds & augment its performance in low velocity winds. The proposed FIS approach is to approximate the power curve by using an adaptive neural fuzzy inference system based on Sugeno model that uses the wind speed and Guide Vane's angle as the inputs and the mechanical power as the output. A hardware prototype of the WECS is used for collecting training data for different guide vane's angle and wind speed conditions. The experimental data obtained is used to train FIS and optimization method used is hybrid method. Thus; WECS with its non-linear parameters is successfully modeled.