Abstract
Vertical axis wind turbines have a place in the small scale renewable energy market. They are not currently implemented on a commercial scale but have found a niche space in urban areas. Here, the turbulent wind conditions and limited space are more easily tapped into with a vertical axis wind turbine. However, the challenges facing these types of turbines have hampered deployment. One of these issues is the fluctuating torque experienced during operation, which can lead to over-designed power trains. Genetic- and gradient-based optimization is applied to an analytical model of a vertical axis wind turbine, in order to reduce the torque fluctuation while attempting to maintain a high power coefficient. The reduction in torque ripple is achieved through a sinusoidal pitching motion of the blades. The torque ripple can be reduced by 10% with a similar reduction in power coefficient.
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