Study on active power control in DFIG-based wind farm

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

Abstract. This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3×4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

This paper proposes a method to control the output power of a wind farm with the aim of improving the transient stability of a multi-machine power system. Doubly fed induction generators (DFIG) are considered for the variable speed wind farms. The variation of the frequency of the DFIG terminal bus is used to modulate the torque reference and thus the output power of the DFIG in the post-disturbance condition. This in turn modifies the electrical power of the nearby alternators and causes improvement of stability. The proposed control technique is validated in WSCC 3-machine 9-bus system and IEEE 16-machine 68-bus system. The study is carried out in PSCAD/EMTDC as well as in MATLAB platforms.

China's wind power capacity is increasing so rapidly that some areas are not able to consume all the produced wind power. Therefore, it has already become the inevitable trend in wind power integration to achieve the controlled operation of wind turbine. This paper first offers a method to predict the maximum generation capacity of every type of wind turbine, including both under-controlled wind turbine and uncontrolled wind turbine. And then a strategy for active power control of wind farm based on the predicting results is proposed. Considering thoroughly the active power fluctuation of wind turbine and the error of the prediction, the strategy adopts both long and short controlling periods to carry out coordinated optimization control. The simulation results show that the strategy can keep the active power output of wind farm in step with the control order, and consequently the generating efficiency is improved. In addition, the strategy is also applicable to the wind farm with some uncontrollable wind turbines, and the controlling precision can be satisfied.

The analysis of reactive power for offshore and onshore wind farms connected to the grid through high-voltage alternating-current transmission systems is considered in this paper. The considered wind farm is made up with doubly fed induction generators (DFIGs). Modeling and improved analysis of the effective reactive power capability of DFIGs are provided. Particularly, the optimal power-tracking constraints and other operational variables are considered in the modeling and analysis of the DFIG reactive power capability. Reactive power requirements for both overhead and cable transmission systems are modeled and compared with each other as well as with the reactive power capability of the wind farms. Possibility of unity power factor operation suggested by the German Electricity Association (VDEW) is investigated for both types of installations. Aggregate reactive power demands on both wind farms are assessed such that the bus voltages remain within an acceptable bandwidth considering various operational limits. The reactive power settings for both types of wind farm installations are determined. In addition, the minimum capacity and reactive power settings for reactive power compensation required for cable-based installations are determined. Several numerical examples are given to illustrate the reactive power characteristics and capability of DFIGs, performance of transmission lines and reactive power analysis for DFIG-based grid-connected wind farms. A summary of the main outcomes of the work presented in this paper is provided in the conclusions section. Copyright © 2012 John Wiley & Sons, Ltd.

This study presents a bi‐level decentralised active power control (DAPC) for a large‐scale wind farm cluster (WFC), consisting of several wind farms for better active power dispatch. In the upper level, a distributed active power control scheme based on the distributed consensus is designed to achieve fair active power sharing among multiple wind farms, which generates the power reference for each wind farm. A distributed estimator is used to estimate the total available power of all wind farms. In the lower level, a centralised control scheme based on the model predictive control is proposed to regulate active power outputs of all wind turbines (WTs) within a wind farm, which reduces the fatigue loads of WTs while tracking the power reference obtained from the upper‐level control. A WFC with 8 wind farms and totally 160 WTs, was used to test the control performance of the proposed DAPC scheme.

Some three-phase short-circuit fault will cause wind turbine generators tripping due to low grid voltage in order to contain transient stability, furthermore, large-scale wind farms tripping can result in severe system oscillation and aggravate the transient instability. In view of this, a static compensator (SVC) is installed in the grid containing large-scale wind farm. A voltage feed-forward control strategy is proposed to adjust the reactive power of SVC compensation and ensure that the grid voltage is quickly restored to a safe range. The mathematical model of the doubly-fed induction wind generator (DFIG) is proposed. The control strategy of DFIG uses PI control for rotor angular velocity and active power. 4-machine system simulation results show that the SVC reactive power compensation significantly improve output active power of large-scale wind farm satisfying transient stability, reduce the probability of the tripping, and improve the utilization efficiency of wind farms.

Since the doubly fed induction generator (DFIG) converters are designed by the slip power (a fraction 25-30% of the total power) to achieve full control of the generator, there is not enough capacity to provide reactive power when the voltage dips below the tolerable grid voltage level at the point of common coupling (PCC). We propose in this paper to use the static synchronous compensator (STATCOM) for compensating the grid voltage dip to improve the power quality in a weak grid or asymmetrical load. The simplified active and reactive power control strategies of a 6 MVA wind farm with DFIG are described. The maximal power point tracking (MPPT) algorithm is implemented. The simulation results using the platform between Matlab/Simulink and PLECS demonstrate the good performance of the wind farm with DFIG and STATCOM, validating the analytical conclusions.

With large wind energy integration into power systems, wind farms begin to influence power systems in a much more significant manner. As wind energy systems utilize different generator technologies from the one utilized in the conventional power plants, the steady-state, transient and small-signal dynamics, as well as, power system stability will thus be significantly affected. The impact of wind energy systems on the power system dynamics and stability is thus of practical importance. As there is a significant increase in installation of wind turbines equipped with doubly-fed induction generator (DFIG) in recent years, a dynamic model of the DFIG wind turbine is firstly developed in this thesis. The model is validated against field measurement data, and the validation gives confidence about the accuracy and applicability of the developed model. DFIG wind farms consist of tens to hundreds of identical DFIG wind turbines increasing the complexity of the wind farm model and simulation time. A novel aggregation technique is developed in this thesis that incorporates a multiplication factor, namely mechanical torque compensation factor (MTCF), to the mechanical torque of the full aggregated wind farm model. The MTCF is initially constructed to approximate a Gaussian function by using fuzzy logic method. By optimizing the MTCF on a trial and error basis, less than 10 percent discrepancy is then achieved between the proposed aggregated model and the complete model. The proposed aggregation technique is then applied to a 120 MVA offshore wind farm comprising of 72 DFIG wind turbines and shows higher accuracy in approximating the wind farm dynamics as it appears at the point of common coupling (PCC) as compared to the full aggregated model. The proposed aggregated model computes faster than the complete wind farm model by 90.3 percent during normal operations and 87 percent during grid disturbances. To overcome the adverse effects due to the fluctuating nature of wind, two wind power smoothing methods are proposed using a fuzzy logic pitch angle controller for a smooth performance with a minimum drop in output power. One method performs partial smoothing with only 4.74 percent drop in the output power while the other method offers complete smoothing with a 8.28 percent drop in output power. The impact of wind energy integration on power system transient stability (PSTS) is studied quantitatively with the transient energy margin (TEM), which is calculated through the evaluation of the transient energy function (TEF). This study is carried out in two ways in the thesis. One is to analyse the impact of transient fault on the DFIG wind turbine as compared to SGs for different factors, like the fault clearing time, the grid coupling, the inertia constant and the voltage sag. The study reveals that transient stability of the DFIG wind turbine is hardly affected by the grid coupling, the inertia constant and the generator terminal voltage sag variations indicating its consistent transient performance within a wide range of these factors. The fault clearing time should be almost 11 percent faster for the system with the DFIG than the synchronous generator (SG). The other is to investigate the impact of the DFIG wind farm on the PSTS with the variation of different factors, which are the voltage sag, the fault clearing time, the load and the wind power penetration level. The study reveals that power systems integrated with DFIG wind farms are sensitive to transient events with high voltage sag, high fault clearing time, low load operation and high wind power penetration level. Machines at different locations individually possess distinct fault response and suffer from a large-scale power imbalance. As a result, reliable operation of DFIG wind farm integrated power systems demands upgraded equipment, such as advanced switchgear, fast breakers/isolators, efficient power reserve systems and advanced reactive power compensating device, etc.

This paper presents a Wide Area Control (WAC) design to enhance the transient stability of Doubly Fed Induction Generators (DFIG) integrated power grid. The proposed WAC design is based on a nonlinear optimal control algorithm using Reinforcement Learning (RL) and Neural Networks (NNs), which optimizes the closed-loop performance of the wind integrated power grid through Approximate Dynamic Programming (ADP). The aim of the WAC is to estimate the global energy function of the system, independent of the contingency, and derive supplementary damping control to augment the excitation system of synchronous generators and local active and reactive power control of DFIG. The controller objective is evolved from transient energy function terms developed for synchronous generators and wind farms within the framework of a coupled oscillatory system. The theoretical results are verified by conducting simulation studies on the modified IEEE 68-bus system with three aggregated wind farms modeled in electromagnetic transient simulator test-bed. It has been shown that the method improves transient stability of the test system and damps the interarea oscillations faster, including the active and reactive power support from DFIG during grid transient conditions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability

Due to that the randomness and fluctuation of wind power may make active power of wind farm difficulty to be controlled, a wind generator dynamic grouping based strategy for active power control of wind farm is proposed. This strategy can classify wind generators into six groups dynamically using the information of ultra-short term power prediction and real-time operation status of wind generators. The power of the two groups with non-monotonous variation trends will be preprocessed, and the principle of control of different wind generator groups is given. Further, detailed allocation method is introduced by analyzing the power regulation ability of wind generators. Case study is carried out to verify the effectiveness of the proposed strategy in a wind farm, the result shows that the power output of wind farm can be smoothened by dynamic and optimal grouping.

Wind turbines are increasingly connected to the bulk power grid through electronic devices. Compared with the dynamic characteristics of traditional power system, transient characteristics are dramatically changed. Recognizing the transient characteristics of large-scale wind power connected to the bulk power grid and proposing practical and feasible transient control strategies for wind turbines are important to the safe and stable operation of current power grid. This paper focuses on the interactive characteristics of double-fed induction generator (DFIG) coupled to the power grid of receiving-end. A DFIG-based wind farm can be reduced to a single DFIG wind turbine by simplifying it, transient dynamics of the wind turbine, such as output fault current characteristics, under different fault levels of power grid are analyzed. Moreover, according to the fault ride through (FRT) standard of wind farms, FRT control strategies of DFIG wind turbines under different fault levels of power grid are presented to form a coordinated fault ride-through control scheme. Finally, a case study was conducted with MATLAB/Simulink. The simulation results verified the effectiveness and feasibility of the interactive characteristics analysis and the proposed FRT control scheme of DIFG-based wind farms connected to the receiving power grid.

With the increasing integration of the wind power into the power system, wind farm are required to be controlled as a single unit and have all the same control tasks as conventional power plants. The wind farm central controller receives control orders from Transmission System Operator (TSO), then dispatch the wind power reference to each wind turbine. One of the most commonly used dispatch methods is to dispatch the wind power reference to each wind turbine proportional to each wind turbine's available wind power without the consideration of the wake effect. The wake which depends on the thrust efficient of upstream wind turbines in the wind farm influences the downstream wind speed which determines the available wind power of the downstream wind turbine. Optimize the wind power production of each wind turbine in the wind farm by the optimization of the pitch angle and tip-speed-ratio of each turbine can increase the total available wind power of the wind farm. This paper proposed a new dispatch method which dispatches the wind power reference to each wind turbine proportional to each wind turbine's optimal wind power to increase the available wind power of the whole wind farm. Particle Swarm Optimization (PSO) is used to obtain the optimal wind power for each wind turbine. A case study is carried out. The available wind power of the wind farm was compared between the traditional dispatch method and the proposed dispatch method with the consideration of the wake effect.