Sub-Synchronous Resonance Damping Control Strategy for DFIG Wind Turbines

Many wind turbine generators (WTGs) are installed in remote, rural areas, where the power grids are usually weak, characterized by unbalanced voltage conditions. If the voltage unbalance is not taken into account in the control system, it will cause poor power quality and poor operating performance of the WTG systems. This paper proposes a novel control scheme to improve the operating performance of a wind turbine equipped with a doubly fed induction generator (DFIG) under unbalanced network voltage conditions. The rotor-side converter (RSC) and grid-side converter (GSC) of the DFIG are controlled in a positive (dq)+ reference frame as well as in a negative (dq)- reference frame. Control of the RSC and GSC in the (dq)+ reference frame is the same as the regular control under balanced network conditions. The supplementary control of the RSC in the (dq)- reference frame minimizes the electromagnetic torque pulsations of the DFIG caused by the unbalanced network voltage; the supplementary control of the GSC in the (dq)- reference frame contributes to balance the total output currents of the DFIG. The proposed control scheme is implemented in PSCAD/EMTDC on a 3.6 MW DFIG wind turbine connected to a power network with unbalanced voltage. Results show that it improves the operating performance of the DFIG wind turbine system.

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.

In this study, an integral sliding mode control approach for controlling the power electronics converters of a doubly-fed induction generator (DFIG) wind turbine system is presented. The power electronics interface consists of back-to-back converters. The rotor side converter regulates the active and reactive powers at the DFIG stator through controlling the stator currents. The stator current dynamics, with respect to the rotor voltages, is developed from the conventional equations of the DFIG model. In this control configuration, the knowledge about the rotor currents is not required, which reduces the use of the current measurement sensors. The grid side converter ensures constant dc-link voltage while transferring the power from the DFIG rotor to the grid. The proposed control approach uses a composition of sliding mode and integral parts to improve the overall performance and robustness against parametric variations and uncertainties. A lab-scale DFIG wind turbine system is used to investigate the proposed control approach efficiency under various operating conditions. The experimental results show the effectiveness of the proposed control approach in achieving control objectives to operate the DFIG wind turbine system.

Background: With the large-scale Doubly Fed Induction Generator (DFIG) wind turbine integrated into the power system, the DFIG inertia response of the wind turbine should be provided. Also, the frequency response should be similar to the conventional generation technologies. This paper investigated the influence of frequency response term and wind speed conditions on system frequency control. Methods: The specific operating conditions of four control strategies, including inertia control, droop control, over speed control and pitch angle control were researched in this paper. Multi-factor coordinated frequency control strategy of DFIG wind turbine was established based on the above researches. The strategy was composed of wind speed ranging from low to high. Results: According to the simulation results, the DFIG wind turbine, which was based on multifactor coordinated frequency control strategy, could respond to the system’s frequency change of power grid, effectively. Conclusion: It helps system frequency return to stable states better and faster than DFIG wind turbine and also could reduce the fluctuation of system frequency.

A quick post fault recovery of wind generator system could be achieved with the help of a series dynamic braking resistor (SDBR). This paper investigates the dynamics, size, optimal location of a series dynamic braking resistor in the doubly fed induction generator (DFIG) structure based on different switching strategy of the braking resistor. Three signals were investigated in switching the SDBR during transient conditions of the DFIG wind generator; the DC-link voltage, rotor current and grid voltage, respectively. Also, three locations were considered in connecting the SDBR in the DFIG system; the stator, rotor and grid side converter, respectively. Furthermore, the optimum size and best insertion time of the SDBR were investigated based on the most effective performance of the SDBR scheme considering its switching signal and position in the DFIG system. Simulations were run in power system computer-aided design and electromagnetic transient including DC (PSCAD/EMTDC). The results show that when the SDBR is connected to the DFIG, improved performance was achieved; however, the stator based SDBR topology in conjunction with a grid voltage actuating signal, fast insertion time and short duration of operation quickly recovered the DFIG wind generator variables during transient conditions.

This work presents the overall model of the doubly fed induction generator (DFIG) and proposes a new control design for the grid side converter of the concerned DFIG plant. The system under study consists of two DFIG wind turbines which are connected to the grid by a bi-directional Z-source inverter. In order to ameliorate the DC link voltage regulation of the DFIG generation plant; particularly, a new supervised control method is introduced and is directly applied to the grid side converter (GSC). In this way, initially, by tuning two control coefficients (CCs) which forms the footprint of uncertainty (FOU), a double-input interval type-2 fuzzy logic (DIT2-FL) is designed for the GSC system. The best setting of the control coefficients for the desired performance of the concerned system is recommended and a new DIT2-FL supervised online adaptive neuro fuzzy inference system (ANFIS) is then developed accordingly. By implementing the supervised controller, the dynamic behavior of the tested wind turbines under two distinct operations for low/high wind speeds is investigated. Detailed experimental outcomes have been presented to ascertain the correctness and accuracy of the suggested control methodology. Compared with the prevalent state-of-the-art techniques, the suggested controller leads to remarkable transient improvement of closed-loop DFIG system.<br/>

Renewable energy production plays a major role in satisfying electricity demand. Wind power conversion is one of the most popular renewable energy sources compared to other sources. Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator (PMSG) and the Doubly Fed Induction Generator (DFIG). The maximum power tracking algorithm is a crucial controller, a wind energy conversion system for generating maximum power in different wind speed conditions. In this article, the DFIG wind energy conversion system was developed in Matrix Laboratory (MATLAB) and designed a machine learning (ML) algorithm for the rotor and grid side converter. The ML algorithm has been developed and trained in a MATLAB environment. There are two types of learning algorithms such as supervised and unsupervised learning. In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions. Simulation results are assessed to demonstrate the efficiency of the proposed system.

풍력발전시스템은 기존의 발전시스템과 매우 다르다. 그러므로 전력계통에 풍력시스템을 연계하기 위해서는 동적특성에 대한 연구가 필요하다. 풍력발전기의 안정도해석은 전력계통의 운영에 있어서 중요 쟁점이다. 기존의 동기발전기만으로 구성된 전력계통의 위상각 안정도는 풍력발전기가 포함되면 그 결과가 달라진다. 즉, 풍력터빈에 연계된 발전기는 대부분 비동기인 유도발전기이기 때문이다. 위상각의 동기화 여부로 판별하는 위상각 안정도는 임계고장제거시간(CCT)을 계산하여 평가한다. 계통해석용 풍력터빈의 모델은 다양하여 그 해석에 어려움이 있으나 지금은 크게 4가지 타입으로 표준화가 되어있다. 본 논문에서는 PSS/E-32에서 제공하는 풍력터빈의 3번째 표준모델인 DFIG(Doubly-Fed induction Generator)모델을 이용하여 풍력단지가 연계된 전력계통의 CCT를 풍력단지의 위치와 용량을 고려하여 분석한다. Wind generation systems are very different in nature from conventional generation systems. Therefore it is necessary to research dynamic characteristics of wind generation systems connected to a power system. The stability analysis of wind turbine generator is an important issue in the operation of the power system. The result of angular stability of the power system that consists of only synchronous generators is different from that of the power system including wind turbine generators. This is due to the fact that generators connected to wind turbines are generally induction generators. The angular stability assessing synchronization of generators is determined by its corresponding critical clearing time(CCT). Wind turbine models for the analysis of power system are varied and difficult to use, but now these are standardized into four types. In this paper, the analysis of the CCT of the power system connected to wind farm considering the location and capacity is performed by using DFIG(Doubly-Fed induction Generator) wind turbine built-in type3 model in PSS/E-32.

Hundreds of megawatt level variable speed Doubly Fed Induction Generator (DFIG) pumped storage turbine will play an important role in voltage and frequency regulation, system stability, active and reactive power control etc.. However, similar but different from the DFIG wind turbines, such power rating and reversible pump-turbine with small capacity converter to control the whole variable speed system is challenging. In this paper, mathematical analysis and modeling of variable speed DFIG pumped storage turbine including reversible pumped turbine, DFIG and back to back converter is build first. On this basis, the control method is presented. The grid side converter stabilizes the dc-side voltage and control the grid-connected current sinusoidal. The rotor-side converter can control and decouple the active power and reactive power independently. Finally, the real time simulation results of 300MW pumped storage turbine based on Real Time Digital Simulator platform indicate the proposed control strategy can achieve good performance in different working conditions of pumping and generation. The system has fast response and good stability.

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 study proposes a hybrid forecast-enabled adaptive crowbar coordination strategy to enhance low-voltage ride-through (LVRT) performance of doubly fed induction generator (DFIG) wind turbines. A unified electro-mechanical model in the αβ/dq frames with dual closed-loop control for rotor- and grid-side converters is built in MATLAB/Simulink (R2018b), and LVRT constraints on current safety and DC-link energy are explicitly formulated, yielding an engineering crowbar-resistance range of 0.4-0.8 p.u. On the forecasting side, a CEEMDAN-based decomposition-modeling-reconstruction pipeline is adopted: high- and mid-frequency components are predicted by a dual-stream Informer-LSTM, while low-frequency components are modeled by XGBoost. Using six months of wind-farm data, the hybrid forecaster achieves best or tied-best MSE, RMSE, MAE, and R2 compared with five representative baselines. Forecasted power, ramp rate, and residual-based uncertainty are mapped to overcurrent and DC-link overvoltage risk indices, which adapt crowbar triggering, holding, and release in coordination with converter control. In a 9 MW three-phase deep-sag scenario, the strategy confines DC-link voltage within ±3% of nominal, shortens re-synchronization from ≈0.35 s to ≈0.15 s, reduces rotor-current peaks by ≈5.1%, and raises the reactive-support peak to 1.7 Mvar, thereby improving LVRT safety margins and grid-friendliness without hardware modification.

Integral Backstepping Control Based on High Gain Observer for DFIG-Based Wind Energy Conversion System

An assessment on the capability of a doubly fed induction generator (DFIG) wind turbine for frequency regulation is presented. Detailed aerodynamic, structural and electrical dynamic models were used in this study. A control loop acting on the frequency deviation was added to the inertia contributing loop in order to enhance the inertia support from the DFIG wind turbine. The possibility of de-loading a wind turbine to provide primary and secondary frequency response was discussed. A frequency droop controller was examined where the droop is operating on the electronic torque set point below its maximum speed and is operating on the pitch demand at maximum speed. It is also shown that by reducing the generator torque set point the DFIG wind turbine can provide high frequency response.

This thesis presents an investigation on system architecture, control and analysis of wind turbine generators to improve grid integration performance. First of all, the main grid connection requirements have been reviewed in terms of safety operation of transmission systems as well as the wind turbine configurations. Due to these requirements, there have been conscious efforts made by wind turbine manufacturers to design grid compatible wind turbines, which are able to improve turbine operating performance and eliminate negative impacts on the utility, with features such as strong fault ride-through capability, flexible voltage regulation and good power quality performance. All these expectations provided the motivation to carry out the research described in this thesis. Doubly-fed induction generator (DFIG) wind turbine technology has been extensively applied by many turbine manufacturers due to the low cost of its partial-scale power electronics converter. However, enhancing ride-through capability under grid fault events has been worldwide recognized as a challenging problem of the DFIG-based wind generation systems. The operating principle and numerical simulation of DFIG-based wind turbine are thus reviewed, which give the fundamentals to analyze the essential behaviors during grid faults. Consequently, a ride-through enhancing solution is proposed based on series compensation principle. The compensator is in series with the stator windings of the DFIG in order to eliminate the impacts caused by grid voltage faults. Furthermore, an advanced ride-through control scheme based on ramp-function injection voltage is developed to significantly reduce the energy capacity requirement of the series compensator, which in turn reduces the capital cost. The performance of the developed architecture has been verified in simulation.

The shortcoming of the DFIG connected to the utility grid is its sensitive to perturbations that may arise internally and grid voltage dip. This paper proposes a nonlinear controller-observer based control of grid-connected Doubly-Fed Induction Generator (DFIG) wind turbine system. The proposed disturbance rejection strategy estimates and compensates for disturbances in the system to improve tracking performance, mitigate against parameter variations and protect the rotor power converters against inrush rotor current and DC-link capacitor overcharge in case of deep grid voltage dips. Improved simulation results of a 1.5MW DFIG are presented and compared with conventional PI-controller performance.