Service quality regulation for wind farms (WFs) can improve power generation and reduce operation and maintenance costs. In this paper, a hierarchical service quality optimal regulation method was proposed to achieve optimal system performance while proactively suppressing potential wind turbine (WT) failures. In the upper-lever, a global optimization problem was formulated based on model predictive control to meet the power commands of the transmission system operator and ensure node voltage safety. Then, the optimal power reference solved by the system-level optimization problem was given as input to the lower-level controller. In the lower-lever, tower displacement and converter temperature associated with power output were modeled as WT health indicators and integrated into a multi-objective optimization problem such that the optimization problem could improve the power flow while proactively suppressing WT failures. Thus, the traditional quality index was extended from individual WT health to system-level performance for the strategic generation of WF with higher reliability and improved economics. Case studies in MATLAB/Simulink demonstrated the effectiveness of the proposed method.

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.

As wind power integration expands, stochastic output fluctuations increasingly affect grid dispatch tracking and voltage stability. To address these challenges, this paper quantifies the active-reactive power capability boundary of doubly fed induction generator (DFIG)-based wind turbine (WT) and develops a hierarchical coordinated framework for dispatch tracking and operational security. First, a DFIG-based WT output boundary model is established by incorporating WT losses, converter capacity, and voltage constraints to characterize the asymmetric feasible region. Based on this model, the upper layer employs model predictive control algorithm to optimize reactive power allocation, aiming to reduce voltage deviations and operating losses, and to determine the energy storage system (ESS) participation mode according to the available active power capacity. The optimized reactive power references, active power limits, and ESS mode signal obtained from the upper layer are passed to the lower layer as boundary constraints and mode-dependent inputs for WT-ESS active-power coordination. In the lower layer, the alternating direction method of multipliers is adopted to coordinate the WT and ESS active power outputs, thereby improving the automatic generation control (AGC) power tracking, smoothing high-frequency power fluctuations, and mitigating fatigue loads. MATLAB/Simulink case studies verify the effectiveness of the proposed method in power secure dispatch and voltage fluctuation suppression.

As wind power integration expands, stochastic output fluctuations increasingly affect grid dispatch tracking and voltage stability. To address these challenges, this paper quantifies the active-reactive power capability boundary of doubly fed induction generator (DFIG)-based wind turbine (WT) and develops a hierarchical coordinated framework for dispatch tracking and operational security. First, a DFIG-based WT output boundary model is established by incorporating WT losses, converter capacity, and voltage constraints to characterize the asymmetric feasible region. Based on this model, the upper layer employs model predictive control algorithm to optimize reactive power allocation, aiming to reduce voltage deviations and operating losses, and to determine the energy storage system (ESS) participation mode according to the available active power capacity. The optimized reactive power references, active power limits, and ESS mode signal obtained from the upper layer are passed to the lower layer as boundary constraints and mode-dependent inputs for WT-ESS active-power coordination. In the lower layer, the alternating direction method of multipliers is adopted to coordinate the WT and ESS active power outputs, thereby improving the automatic generation control (AGC) power tracking, smoothing high-frequency power fluctuations, and mitigating fatigue loads. MATLAB/Simulink case studies verify the effectiveness of the proposed method in power secure dispatch and voltage fluctuation suppression.