This paper proposes a decentralized Volt/Var control method based on variable gradient projection (VGP) for the permanent magnet synchronous generator (PMSG)-based wind farm (WF). The aim is to achieve a fast Var response of each wind turbine (WT) to depress voltage fluctuations inside the WF caused by the stochastic wind and voltage disturbance of external girds. The gradient projection (GP) method is used in the PMSG-based WF to regulate terminal bus voltage within a feasible range in a fully decentralized manner while the dynamic characteristics of the PMSGs and grid side converters (GSCs) are taken into consideration. Moreover, according to the difference of the available power distribution among the WTs inside the WF, the adaptive and variable gain method is designed for the GP method, which can achieve maximum Var margin of each local WTs during the real-time operation of the WF. With the proposed control method, the PMSG-based WTs are coordinated to achieve near-global optimal performance only with local measurements. The voltage fluctuations of buses are reduced while the Var margin of WT converters is maximized. The WF with $24 \times 5$ MW WTs is tested in Matlab/Simulink with the real-time fully nonlinear AC power flow environment.

Due to the flexibility and economy, energy storage systems (ESSs) have been widely deployed in wind energy power plants for power regulation. The coordination of generators and ESSs is critical for the wind farm (WF) operation. In this article, a two-stage voltage control is proposed in WFs equipped with distributed ESSs to achieve a near-global optimal performance without any centralized computations and communication links. To achieve it, the WF control is divided into two regions based on the demand-supply relationship of reactive power to consider the different operating conditions and multiple optimization objectives. The distributed battery-supercapacitor hybrid ESSs are deployed in each wind turbine within the WF to regulate the power output while ensuring voltage stability and enhancing the Var support capability. An optimization problem is formulated considering the node voltage deviation, the state of charge, and the cost of the storage units, which can be simply calculated using only local measurements through a decoupling method based on network topology characteristics. Further, an asynchronous response framework is designed for the battery and the supercapacitor to achieve a better economic benefit. Case studies in MATLAB/Simulink verify the effectiveness and reliability of the proposed method.

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.

This paper proposes a gradient correction-based decentralized optimal Var/Volt adaptive fault-tolerant control (GC-AFTC) method for wind farms (WFs). The aim is to ensure the decentralized optimal Var/Volt operation of wind turbines (WTs) inside a radial topology WF even under the fault condition due to several WTs being tripped. When some faults occur and several WTs are tripped, the decentralized optimal Var/Volt operation of WF still can be maintained without the reconstruction of the incidence matrix and any quantitative fault information of the fault WTs. To achieve the fault-tolerant optimal Var/Volt control, the impact of random off-grid events (ROEs) on the WF collection system is analyzed at the farm level. Subsequently, a unified GC method is derived based on the gradient projection (GP) method. The transient Var related to the ROE is incorporated into the GC method to enhance the capability of decentralized online applications of WTs. Additionally, to improve the dynamic performance of WTs, a personalized step coefficient is designed within a stable range, which is utilized in each local WT controller during the GC-AFTC period. Case studies show that the proposed method can drive the WF system towards a better operating point only with local measurements in normal and fault conditions.

Meeting the power demand from the transmission system operator is an important objective for power dispatch, which introduces a power supply-demand equality constraint coupling all the wind turbines among the wind farm into the optimization problem. For a large-scale wind farm, processing the global equality constraint in a centralized or distributed framework is time-consuming and computationally complex. Here we considered the fast and localized execution issue of the power optimal dispatch problems. A completely decentralized dynamic system was designed to optimize power flow while satisfying the electricity supply constraints. A voltage optimization problem with the global power constraints was decoupled into local wind turbine controllers based on the node-dependence nature, which is an inherent characteristic of wind farms and was fitted to the power sensitivity matrix in this paper. The local optimization problem was solved iteratively using the gradient projection method, and the system converged linearly to the equilibrium point. The simulations for the case studies performed in Simulink demonstrate that the proposed method achieves a near-global optimal performance using only local measurements.

The knowledge about a network model is essential for control. However, the topology of wind farms (WFs) may be changed due to random off-grid events of wind turbines. This article develops a two-stage local method for WFs to achieve the Var/Volt coordinated optimization with consideration of topology changes, which provides an efficient solution when limited data are available for identification and control. In the first stage, considering the operational characteristics of WFs, a mixed integer optimization problem is formulated in each local controller to uncover the physical network topology. The problem is then solved and driven toward the optimum by an online recursive search algorithm. The proposed algorithm is intended to identify the physical network layout only using local measurements at no loss of accuracy. In the second stage, a completely decentralized Var/Volt control method is designed with the updated topological knowledge, aiming to achieve a fast Var response and to reduce voltage deviation caused by the stochastic wind power and voltage disturbance of external girds. Case studies in MATLAB/Simulink verify the robustness and effectiveness of the proposed method within a wide range of working conditions.