Power correlation for vertical axis wind turbines with varying geometries

Abstract

In this paper, a new predictive model that can forecast the performance of a vertical axis wind turbine (VAWT) is presented. The new model includes four primary variables (rotor velocity, wind velocity, air density, and turbine power output) as well as five geometrical variables (rotor radius, turbine height, turbine width, stator spacing, and stator angle). These variables are reduced to include the power coefficient (Cp) and tip speed ratio (TSR). A power coefficient correlation for a novel VAWT (called a Zephyr Vertical axis Wind Turbine (ZVWT)) is developed. The turbine is an adaptation of the Savonius design. The new correlation can predict the turbine's performance for altered stator geometry and varying operating conditions. Numerical simulations with a rotating reference frame are used to predict the operating performance for various turbine geometries. The case study includes 16 different geometries for three different wind directions. The resulting 48 data points provide detailed insight into the turbine performance to develop a general correlation. The model was able to predict the power coefficient with changes in TSR, rotor length, stator spacing, and stator angle, to within 4.4% of the numerical prediction. Furthermore, the power coefficient was predicted with changes in rotor length, stator spacing, and stator angle, to within 3.0% of the numerical simulations. This correlation provides a useful new design tool for improving the ZVWT in the specific conditions and operating requirements specific to this type of wind turbine. Also, the new model can be extended to other conditions that include different VAWT designs. Copyright © 2010 John Wiley & Sons, Ltd.