Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms

Abstract

Wind energy plays a crucial role in the quest for sustainable energy solutions. However, optimizing the efficiency of wind energy utilization remains a significant challenge. Wake steering, a key strategy in the field, offers the potential to address this challenge. This study introduces an innovative site-specific wake steering framework that incorporates a wake superposition model for wake steering, a machine-learning based fatigue and power predictor and a multi-objective optimizer to both enhance total power generation and mitigate fatigue loads within wind farms. The wake superposition model, developed and validated here, successfully replicates secondary wake steering effects and provides a new solution for calculating superimposed transverse velocity. The study comprehensively considers and implements constraints based on physical laws. Analysis of inflow speed and turbulence levels reveals that wake steering can continue to enhance total power output. Power enhancement can reach up to 18% at lower turbulence levels and still achieve significant increases even when inflow speeds exceed rated values, with only marginal increases in fatigue loads. Lower turbulence levels improve optimization results at the expense of heightened structural loads, while higher turbulence levels lead to diminishing power enhancement and additional fatigue loads. Examination of wind turbine spacing shows that smaller intervals yield substantial power enhancement, with improvements of up to 51.7%, although the effect diminishes as intervals increase and wake recovery takes place. In conclusion, the proposed site-specific wake steering framework offers an efficient means of balancing enhanced wind farm power output and structural integrity, representing a significant advancement in wind energy optimization.