Research on Optimized Active Power Allocation Strategy for Wind Farms Considering Fatigue Damage of Wind Turbines

With the increasing number of large-scale wind farms, optimizing active power allocation based on the cumulative fatigue damage of wind turbines has become the key to improve power generation efficiency and reduce the failure rate. Firstly, to address the problems of high computational complexity and long time-consumption of the existing computational models in assessing the fatigue damage of wind turbines, a “stress cycle identification method based on improved peaks and valleys detection and cycle matching” is proposed, which identifies the peaks and valleys by analyzing the sign changes of the slopes of the neighboring stress waves and determines the local extreme value points, thus enhancing the accuracy and speed of the assessment model. It enhances the accuracy and rapidity of the evaluation model. Then, this paper establishes a tower thrust and main shaft torque estimation model related to the wind speed and output power of wind turbine operation by combining the wind turbine momentum theory and Bates theory, and adopts the NSGA-II algorithm to minimize the total sum of fatigue damage of the main shafts and towers of all wind turbines for the power allocation. For the measurement noise and communication delay that occurs during the scheduling process, CEEMDAN joint wavelet threshold denoising method is used to process the measurement noise, and MPC is used to solve the communication delay problem and enhance the robustness of the model. Finally, the numerical simulation results show that the strategy proposed in this paper significantly reduces the fatigue damage and maintenance cost of the wind turbine, and reduces the impact of signal noise and communication delay on real-time scheduling.