This paper presents a novel magnetically levitated Vertical Axis Wind Turbine (VAWT) with Omni-directional guide vanes which act as wind concentrator as well as wind shield. Three rotating wind guide vanes/flaps of concave shape are placed on the outer periphery of the turbine. Venturi effect is produced by dynamically changing the angles of the flaps by closed loop control. The vanes protect the turbine from high velocity winds & increase its performance under low velocity winds. There are two modes of guide vanes operation. In the first mode, all the vanes are synchronized and angle of guide vanes is same for all. In the second mode, an additional sensor reads direction of wind and accordingly the system aligns guide vanes individually. A microcontroller based hardware prototype of the turbine has been fabricated and tested for different conditions. The magnetic suspension results in reduced frictional losses. The results obtained validate the advantages of the fabricated turbine as compared to other configurations.

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.