Yaw Wake Steering for Simultaneous Power Maximization and Component-Level Fatigue Balancing in Offshore Wind Farms

Wake effects in offshore wind farms reduce power output and cause uneven fatigue among turbines, thereby increasing operation and maintenance (O&M) costs. This study proposes a multi-objective wake steering strategy that simultaneously maximizes wind farm power generation and balances component-level fatigue loads across turbines. First, a one-minute accumulated fatigue damage (FD) lookup table is developed for key turbine components to enable real-time fatigue quantification. Using a Gaussian wake model, a multi-objective yaw optimization model is then formulated. Second, an improved Chaotic Evolution Optimization algorithm is developed by integrating a local search operator with a hybrid triggering mechanism to efficiently solve the optimization model. The optimization maximizes farm power while minimizing the weighted relative standard deviation (RSD) of component-level FD across turbines. Finally, a representative offshore wind farm with a parallelogram layout is constructed as a simulation case, and the proposed wake control strategy is validated under real-world inflow conditions using measured wind data from an offshore wind farm. The proposed multi-objective strategy increases farm power by 31.67% relative to MPPT, comparable to the power-only strategy (31.68%). Relative to MPPT, the proposed strategy decreases the RSD from 0.362 to 0.065 for the blade root, from 0.058 to 0.001 for the low-speed shaft, and from 0.318 to 0.051 for the tower base, without increasing the overall accumulated fatigue load of the wind farm. This study highlights the potential of component-level fatigue balancing to reduce O&M costs and enhance the economic viability of offshore wind farms.