An optimized management strategy for wind turbines under wind curtailment condition considering wake effect

Wind farm power generation is limited under wind curtailment conditions. An unreasonable selection of the operating wind turbines can cause severe wake effects, decreasing power generation efficiency and increasing fatigue loads on downstream turbines. This study proposes an optimized management strategy for curtailed wind farms to minimize the wake effects. The chaotic binary particle swarm algorithm is employed to determine whether a wind turbine should be started up or shut down. The velocity and turbulence intensity distribution in the wake are computed by three-dimensional models. A wind farm in northern China is studied to investigate the behavior of the proposed strategy. The obtained results show that at a wind speed of 7 m/s, the average aerodynamic efficiency of the wind farm is increased by 4.38 % and the average maximum turbulence intensity is reduced by 29.31 %. The turbulence intensity across the rotors decreases and becomes more evenly distributed. Compared with the uniform load sharing strategy, the proposed strategy reduces the average operating time of turbines by 24 %, which is substantially beneficial for extending the turbines’ lifespan. Finally, the proposed strategy is more suitable for scenarios exhibiting a higher curtailment rate, where the optimization potential is sufficient.