Rapid power and torque fluctuations in time-varying conditions increase the fatigue load and failure rate of wind turbine (WT) gearboxes. In this study, an optimal power control method is proposed for a wind farm (WF) to improve the power flow and service quality, allowing the WF to track the power demand instructions from the transmission system operator while minimizing the fluctuations of vibration displacements inside the gearbox. A comprehensive dynamic model of the gearbox is developed by analyzing the transmission mechanism of key gearbox components, such as the planet carrier, planet gears, sun gears, and spur gears, describing the correlation between the internal vibration and mechanical torque and power output. Then, an optimal power control problem is formulated based on model predictive control to suppress the fatigue load while tracking power. Furthermore, a fatigue evaluation system is built based on the real-time vibration state inside the gearbox to characterize the service quality of WTs and guide the power generation of the WF. This approach provides a safety-oriented boundary regarding the WT fatigue load in the optimal power dispatch issue of WFs to suppress potential WT failures. Case studies in MATLAB/Simulink demonstrated the effectiveness of the proposed method.

The increasing integration of wind turbines into the power grid has reduced the system frequency stability, necessitating the integration of energy storage systems in primary frequency regulation. This paper proposes an MPC-based control method to optimize the frequency response of a combined wind–storage system. An evaluation system is also developed to characterize frequency response stability and guide power dispatch. First, the system model and state-space equations for MPC are established. Then, the control strategy is proposed to achieve the combined objective of minimizing power variation and frequency deviation. Finally, frequency stability is assessed using the evaluation system. MATLAB/Simulink case studies confirm the effectiveness of the proposed method in enhancing frequency regulation performance. The results show that this control strategy not only accelerates the response speed of the system frequency but also reduces its fluctuations, thereby improving the frequency stability of the system.

In this paper, a non-iterative sensitivity-based zsolve the distribution network reconfiguration (DNR) problem and ensure the security of the switch operation process, while maintaining the node voltage within the feasible range. The NSSE method involved the sensitivity-based network reconfiguration (SNR) stage and sensitivity-based power optimization (SPO) stage. In the SNR stage, the computational speed was improved by considering the dynamic voltage sensitivity with respect to the compensated power analysis, while the network power losses and number of switching times were minimized. The closed-loop and open-loop operation model was developed to solve the compensated power for simulating the switch operation process. Moreover, the maximum loop current was set as a constraint to avoid the equipment risk of switching operation. Each switch operation process can be formulated as integer quadratic programming (IQP) to obtain the optimal network topology and switch operation sequence with the non-iterative calculations. In the SPO stage, the active and reactive power outputs of distributed generations (DGs) were optimized to regulate the node voltage and reduce the active power curtailment. The effectiveness of the proposed NSSE method on the calculation speed and accuracy was validated in MATLAB.

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of “key component–wind turbine–wind farm” based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the “maximum membership degree”, which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms.

In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions.