Wind turbine blade leading edge erosion (LEE) is potentially a significant source energy loss and expense for windfarm operators. This study presents a novel approach to characterizing LEE potential from precipitation across the contiguous USA based on publicly available National Weather Service dual-polarization RADAR data. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments.

The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.

In this paper, the multi-objective genetic algorithm is coupled with the class shape transform method to optimize the wind turbine airfoil, and the trailing edge is thickened for the optimized airfoil. The results show that the lift coefficient and lift-to-drag ratio are improved at all angles of attack and the stall is delayed. And the blunt trailing edge airfoil has better lift-to-drag characteristics than the original airfoil and the optimized airfoil.

Offshore wind farm clusters cause reduced wind speeds in downstream regions which can extend over more than 50 km. We analysed the impact of these so called cluster wakes on a distant wind farm using remote sensing wind measurements and power production data. Cluster wakes caused power losses up to 55 km downstream in certain atmospheric states. A better understanding of cluster wake effects reduces uncertainties in offshore wind resource assessment and improves offshore areal planning.