通过协同偏航与尖速比优化实现风电场主动尾流控制

Abstract Aerodynamic loss, also known as wake loss, is the most significant loss hindering wind energy from rising beyond its current single-digit percent contribution to global electricity generation . This research explores the effectiveness of integrating real-time yaw and tip-speed ratio (TSR) optimizations to address this issue. This combined adaptive control strategy, which has never been investigated before, amplifies the advantages of individual yaw and TSR optimizations while addressing some of their challenges. The proposed adaptive optimization strategy leverages particle swarm optimization and the FLOw Redirection and Induction in Steady State (FLORIS) model to continuously adjust individual wind turbines' yaw angle and TSR based on real-time wind conditions. This optimization algorithm enhances the farm's energy production by dynamically misaligning a subset of the turbine's rotor to redirect their wake away from their downstream counterparts. Simultaneously, it decreases the tip-speed ratio of a different subgroup of turbines to non-optimal conditions, leaving more power in their wake for their downstream counterparts to harvest. According to the models, implementing the integrated yaw-TSR optimization at Lillgrund, an offshore wind farm in Sweden, increased the annual energy production (AEP) by 4.85 %. This improvement surpassed the outcomes predicted for individual yaw or TSR optimizations, which yielded a 3.9 % and 2.7 % increase, respectively. This also decreased the farm-averaged yaw misalignment and TSR, which offer essential structural and environmental advantages.