Doubly Fed Induction Generator System Research Articles

Control of DFIG-based wind power generation system under unbalanced grid voltage conditions

Doubly Fed Induction Generator (DFIG)-based wind turbines have become increasingly popular in recent years due to their capacity to operate at varying speeds. Weaknesses in the DFIG system can arise from issues with the power grid due to the stator's direct connection and the excitation converter's power rating limitation. Under situations of unbalanced grid voltage, this study aims to explore the efficacy of the Direct Power Control (DPC) approach in managing wind turbine systems based on DFIG. Throughout the experimental investigation, we evaluated the system in standard and unbalanced grid voltage settings. MATLAB/SIMULINK simulations implement DPC, specifically tailored for a 9 MW DFIG-based wind farm. The results of these simulations show that the changed control method effectively reduces torque oscillations by making it possible to create active and reactive power references for the rotor-side converter. This eliminates the requirement for sequence component excitation, which was previously necessary. Furthermore, the research highlights the intrinsic link between control techniques and grid circumstances, showing this connectivity's crucial role in improving wind energy systems' stability and operational efficiency based on DFIG.

Bursting oscillation characteristics and adaptive high‐order differential feedback controller of doubly fed induction generator system

SummaryTo address the problem of bursting oscillations in doubly fed induction generator (DFIG) systems under external disturbances, an analysis method based on fast‐slow dynamics and a control strategy based on an adaptive high‐order differential feedback controller (AHODFC) is proposed. The fast‐slow dynamics analysis method was adopted to reveal the formation mechanism of bursting oscillations from the DFIG system owing to the multi‐timescale coupling effect of perturbation, that is, the oscillation is in the form of a bursting oscillation with alternating large‐amplitude oscillation and micro‐amplitude oscillation. A control strategy based on the AHODFC with a high‐order differentiator (HOD) was proposed to address the problem of bursting oscillations in the DFIG system. The control strategy of the AHODFC has smooth control‐rate characteristics, does not depend on the exact mathematical model of the controlled system, and has good applicability and robustness to various types of bursting oscillations caused by different disturbance amplitudes and system parameters. Our simulations verified the feasibility and effectiveness of the proposed method.

Neural network predictive control for fault detection and identification in DFIG with SMES for low voltage ride-through requirements

The increasing reliance on renewable energy sources brings to light the operational challenges of doubly fed induction generators (DFIGs), particularly under grid disturbances. This study introduces an innovative approach employing Neural Network Predictive Control (NNPC) for fault detection and identification (FDI) in DFIG systems, integrated with Superconducting Magnetic Energy Storage (SMES) to meet Low Voltage Ride-Through (LVRT) requirements. Our method uniquely combines NNPC for dynamic wind speed prediction and precise control of rotor-side and grid-side converters with the stability enhancement offered by SMES. Simulation results demonstrate a notable improvement in DFIG performance under variable wind conditions and grid failures. The system-maintained voltage stability at the Point of Common Coupling (PCC) with less than 5% deviation during transients and ensured consistent power output, marking a significant advancement in LVRT compliance. However, the model assumes steady-state grid conditions and does not account for extreme weather scenarios, indicating areas for future research. This study's findings contribute valuable insights into the robust operation of DFIGs within modern renewable energy grids.

Performance Analysis of Photovoltaic and Wind Turbine Grid-Connected Systems under LVRT Conditions

The integration of grid-connected renewable energy systems has gained significant attention and introduces several challenges and considerations. One of these challenges is ensuring the reliable and stable operation of these systems under various grid conditions. For example, faults at the grid could lead problems such as DC-link over-voltage and AC over-current that may cause disconnection or damage to inverter. This paper presents a comprehensive analysis of the performance of photovoltaic (PV) and doubly-fed induction generator (DFIG) wind turbine grid-connected systems under low voltage ride-through (LVRT) conditions. The study aims to investigate their behavior, and stability during LVRT events and provide insights for enhancing their grid integration capabilities. The PV and DFIG systems are modelled and simulated using MATLAB Simulink under three difference conditions, with and without using reactive current injection and DC chopper circuit. Various performance parameters, including grid voltage, grid current, DC-link voltage, active power, and reactive power, are analyzed to assess the system's behavior and compare their responses. The principal results reveal distinct performance characteristics of the PV and DFIG systems. The PV system shows higher overshoot currents, over-voltage, and significant drops in active power during fault occurrences, while the DFIG system exhibits lower overshoot currents and better stability in the DC-link voltage. Reactive power responses differ between the systems, with the PV system demonstrating a higher capability for support. The implementation of DC chopper shows more effective in the reduction of DC-link voltage and overshoot grid current in the PV system compared to the DFIG system.

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

Wind Power Frequency Control in Doubly FED Induction Generator Using CFMPC-FOPID Controller Scheme

Because the majority of wind turbines operate in maximum output power tracking mode, power system frequency cannot be supported. However, if the penetration rate of wind power increases, the system inertia related to frequency modulation may decrease. In addition, frequency stability will be severely affected in the event of significant disturbances to the system load. Due to the high penetration of wind power in isolated power systems, this study suggests a coordinated frequency management approach for emergency frequency regulation. In order to prevent the phenomenon of load frequency control in doubly fed induction generators (DFIGs), a unique efficient control scheme is developed. The Cascaded Fractional Model Predictive Controller coupled with Fractional-Order PID controller (CFMPC-FOPID) is developed to provide the DFIG system with an efficient reaction to changes in load and system parameters. The proposed controller must have a robust tendency to respond quickly in terms of minimum settling time, undershoot, and overshoot. Nonlinear feedback controllers are designed using frequency deviations and power imbalances to achieve the reserve power distribution between generators and DFIGs in a variety of wind speed conditions. It makes upgrading quick and easy. In Matlab/Simulink, a simulation model is built to test the viability of the suggested approach.

The stochastic stability and H∞‐fuzzy control of stochastic bifurcation of a doubly‐fed induction generator

AbstractConsidering the dynamic behavior of doubly‐fed induction generators (DFIGs) under the influence of random factors, this paper not only analyzes the phenomenon of stochastic instability and bifurcation of a DFIG dynamic variable in its random space when they are affected by environmental noise, but also proposes a method based on the Tkagi–Sugneo (T–S) fuzzy control strategy to control its stochastic bifurcation. First, a four‐dimensional stochastic dynamic DFIG model is established by using multiplicative white noise to simulate the influence of environmental noise on electrical variables, and stochastic central manifold theory is used to reduce the dimensionality of a planar model in the bifurcation neighborhood. Then, the stochastic stability of the model is investigated based on singular boundary theory, while the steady‐state probability density of the stochastic DFIG is determined using the Fokker–Plank–Kolmogorov equation to obtain the location and probability density of its stochastic P‐bifurcation. Finally, the influence of stochastic bifurcation behavior is eliminated by H∞‐fuzzy output feedback control. The numerical simulation results indicate that the location and probability of stochastic bifurcation in a DFIG will vary with the change in noise intensity, and the bifurcation parameter values and stochastic stability domain are obtained. The harm caused by random factors can be solved based on H∞‐fuzzy output feedback, which provides a theoretical basis for the stable operation of the DFIG system.

An Argument-Principle Based Stability Assessment Method for Grey-Box DFIG Systems

Considerable efforts have been made to analyze the small-signal stability of doubly fed induction generator (DFIG) systems. However, commercial confidentiality and frequency coupling make the DFIG system a grey-box multiple-input-multiple-output (MIMO) system with highly challenging stability analysis. This paper proposes an Argument-principle based stability assessment method to analyze the stability of the grey-box DFIG system. The frequency sweeping technique is first used to acquire the MIMO model of the black-box device, as well as the determinant of the system's return difference matrix. Then a stability criterion based on the determinant trajectory is presented. This criterion applies to the stability analysis of grey-box MIMO systems without detailed system models. Further, a critical-pole estimation method with trajectory information is developed to assess the dominant mode of the target system. The simulation and hardware-in-loop experiment results demonstrate the effectiveness of the proposed method. Finally, some concerns about this method, such as model selection, estimation errors and application potential, are thoroughly analyzed and clarified.

Evaluation of the possibility of chaos for doubly-fed induction generator in wind power generation system

<span lang="EN-US">The doubly-fed induction generator (DFIG) has the advantages of compactness and low cost, so it has been applied more and more in practice. However, the structure of the DFIG system is complex, multivariable, nonlinear, and difficult to control. Chaos is a phenomenon that only occurs with nonlinear dynamical systems, sensitive to initial conditions and aperiodic but it is governed by deterministic laws, unlike random perturbations. For most powertrain systems, chaos causes disadvantages, reduce performance, and even destruction of the system. By method of analysis based on theory and simulation, the paper will demonstrate the chaotic phenomenon for DFIG when the parameters of the system change into a certain value. From that set of parameters, the controller is designed to avoid or eliminate that chaotic behavior, so that the system works more reliably and with better quality.</span>

Optimization of Sliding Mode Control for Doubly Fed Induction Generator Systems Using Particle Swarm and Grey Wolf Algorithms

This study proposes the utilization of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to optimize the gains in sliding mode control (SMC) for a doubly fed induction generator (DFIG) system. The primary aim is to enhance reference tracking, improve overall system performance, and ensure system stability. The controllers’ performance is assessed by evaluating tracking performance and stability through the analysis of noise signals in the stator’s active and reactive power controllers. The experiments involve 10 search agents in both PSO and GWO algorithms, with a maximum of 100 iterations. Statistical results reveal that the PSO algorithm demonstrates better convergence stability and yields lower fitness function values, particularly in the case of IAE. Simulations demonstrate that SMC tuning with PSO leads to satisfactory dynamics for active and reactive powers, characterized by fast response and no overshoot. Performance metrics, including IAE and ISE indices, are employed to assess the control strategies, consistently showing that the SMC with PSO controller outperforms the SMC with GWO controller in terms of criteria values. The proposed control strategy utilizing PSO optimization effectively enhances reference tracking and overall performance in DFIG systems.

Limits of reactive power compensation of a doubly fed induction generator based wind turbine system

The doubly fed induction generator (DFIG) systems feature a significant amount of free power capacity that may be used for reactive power adjustment when they are put into practical use. This change, which is occasionally overlooked, is a significant one. Using DFIG systems for wind turbines (WT), this paper explored strategies for reducing and using reactive power. In order to investigate the power characteristic and how it is regulated in DFIG systems, a mathematical model for the steady-state performance of DFIG WT has been developed and presented. Here is a detailed derivation of the limiting range of DFIG's reactive power capacity as well as the physical constraints on reactive power output. The distribution of the DFIG WT at a distribution network's end is demonstrated by a simulation example. Within this simulation, reactive power management strategy, load fluctuation, and the change in wind speed are all taken into consideration. Due to the possibility of a rise in the voltage at the access point, can concluded that both acceptable and efficient to use DFIG WT's reactive power capabilities as an additional continuous reactive power source for effectiveness.

Sequential frequency regulation strategy for DFIG and battery energy storage system considering artificial deadbands

To address the issues of the mechanical stress of doubly-fed induction generator (DFIG) and the service life of energy storage systems (ESSs) resulting from excessively and frequently participate in grid frequency regulation (GFR), this paper suggests a sequential GFR of the DFIG and ESS considering the artificial dead-bands. Firstly, the system frequency deviation expression is derived considering the dead-band to analyze the influences of the dead-bands on the grid frequency dynamics. Secondly, based on the artificial dead-bands, the GFR process is divided into three zones: the GFR zone of DFIG, the transaction zone of DFIG and ESS, and the GFR zone of ESS. To avoid the large mechanical stress of the wind turbines and shallow charging and discharging phenomenon of the ESS, the DFIG provides GFR function to mitigate the system frequency fluctuations, while ESS provides frequency regulation function to arrest the grid frequency variation taking into account the condition of state-of-charge constraints for severe frequency disturbances. Finally, simulations are conducted to verify the effectiveness of the proposed strategy under varying wind speed conditions combined with large disturbance. The results show that the proposed sequential GFR can maintain the frequency support while reducing the negative effects of mechanical fatigue of the DFIG and service life of ESS.

Parallel Impedance-Reshaping Control Strategy for Suppressing Wide Range High-Frequency Resonance of Shunt Compensated DFIG System

The shunt compensated grid (SCG) connecting with doubly-fed induction generator (DFIG) system easily suffers from high-frequency resonance (HFR). Existing studies reshape the DFIG based on a pre-calculated frequency to suppress the HFR. When facing voltage disturbance, shunt capacitors are added to the grid which greatly changes the resonance frequency, probably resulting functional failure of HFR suppression. In this paper, a parallel impedance-reshaping control (PIRC) strategy for the rotor side converter (RSC) and grid-side converter (GSC) control is proposed to suppress the HFR of the DFIG-SCG system when the shunt capacitor varies in a wide range. A high-order coefficient is combined with the original impedance of the RSC-induction generator (RSC-IG) side, and a damping controller is designed to reshape the impedance of the DFIG system. Based on the parallel structure of the equivalent impedance of the GSC side and the RSC-IG side, the impact of the shunt capacitor to the controller can be eliminated, thus providing effective HFR suppression in a wide frequency range. Case studies demonstrate the advantage of the PIRC in HFR suppression and harmonic suppression.

Impedance Reshaping Band Coupling and Broadband Passivity Enhancement for DFIG System

Doubly fed induction generator (DFIG)-based wind power systems incur a broadband negative resistive characteristic, resulting in resonance risks when connected with weak grid or diverse grid transmission infrastructure. The dominant factor and underlying mechanism of this broadband negative resistive characteristic are analyzed based on the passivity-based stability assessment. It is then noticed that the negative resistive region within low frequency, middle frequency and high frequency are closely linked to the current controller, phase locked loop and system delay, respectively. This article reveals the impedance reshaping band coupling phenomenon to facilitate some passivity-based designs, then puts forward a broadband passivity enhancement method for DFIG system. The experimental results validate the aforementioned conclusions and the effectiveness of the proposed passivity enhancement method.

Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition

This research explores a distinctive control methodology based on using an artificial neural predictive control network to augment the electrical power quality of the injection from a wind-driven turbine energy system, engaging a Doubly Fed Induction Generator (DFIG) into the grid. Because of this, the article focuses primarily on the grid-integrated wind turbine generation’s dependability and capacity to withstand disruptions brought on by three-phase circuit grid failures without disconnecting from the grid. The loading of the grid-integrated power inverter causes torque and power ripples in the DFIG, which feeds poor power quality into the power system. Additionally, the DC bus connection of the DFIG’s back-to-back converters transmits these ripples, which causes heat loss and distortion of the DFIG’s phase current. The authors developed a torque and power content ripple suppression mechanism based on an NNPC to improve the performance of a wind-driven turbine system under uncertainty. Through the DC bus linkage, it prevented ripples from being transmitted. The collected results are evaluated and compared to the existing control system to show the advancement made by the suggested control approach. The efficacy of the recommended control methodology for the under-investigation DFIG system is demonstrated through modelling and simulation using the MATLAB Simulink tool. The most effective control technique employed in this study’s simulations to check the accuracy of the suggested control methodology was the NNPC.

Enhancement of LVRT Ability of DFIG Wind Turbine by an Improved Protection Scheme with a Modified Advanced Nonlinear Control Loop

One of the problems with the doubly-fed induction generator (DFIG) is its high vulnerability to network perturbations, notably voltage dips, because of its stator windings being coupled directly to the network. As the DFIG’s stator and rotor are electromagnetically mated, the stator current peak occurs during a voltage dip causing an inrush current to the critical converter back-to-back and an overload of the DC-link capacitor. For this purpose, a series of researchers have achieved a linear and non-linear controller with a crowbar-based protection scheme. With this type of protection, the Rotor Side Converter (RSC) is disconnected momentarily, and consequently, its control of both the active and reactive output power of the stator is totally lost, leading to incorrect power quality at the point of common coupling (PCC). In this document, a robust nonlinear controller by Advanced Backstepping with Integral Action Control (ABIAC) is initially employed to monitor the rotor and the network side converters under normal network operations. In the presence of a network fault, an improved protection scheme (IPS) is tacked on to the robust nonlinear control to help enforce the behavior of the DFIG system to be able to overcome the fault. The IPS, which is formed by a crowbar and an RL series circuit, is typically located in the space between the rotor coils and the RSC converter. Compared to a standard crowbar, the developed scheme is successful to limit the rotor transient current and DC-link voltage, also an RSC disengagement to rotor windings can be prevented during the fault. Furthermore, the controllers of both the RSC and the Network Side Converter (NSC) are modified to boost the supply voltage at the PCC. A comparative study is also performed between the IPS without and with modification of the reactive power control loops. The simulation results mean that with the modified controllers during the fault, the amount of reactive power sustainment with ABIAC at the PCC is optimized to 17.5 MVAr instead of 15 MVAr with proportional-integral control (PIC). Therefore, the voltage at the PCC is fort increased in order to comply with the voltage requirements of the farm and absolutely to maintain the connection to the network in case of voltage dip.

APPLICATION OF STATIC SYNCHRONOUS COMPENSATOR (STATCOM) AS REACTIVE POWER CONTROL IN DFIG WIND FARM AND GRID INTERCONNECTION SYSTEMS

The most difficult aspect of optimizing a wind farm is creating an accurate wind farm model, especially if the farm is connected to the grid. The inconsistency and unpredictability of wind speeds exacerbates this issue. When dealing with wind farms, it is possible that the reactive power addition capabilities of individual wind turbines are insufficient to meet network requirements. This is due to cable losses and line losses between the wind farm and PCC. This study employs a doubly fed induction generator (DFIG) and a Static Synchronous Compensator (STATCOM) compensator to keep the output voltage amplitude more constant. Using two PI controller loops, the STATCOM will generate reactive (capacitive) power if the DFIG voltage is lower. The STATCOM will then absorb reactive (inductive) electricity if the DFIG system voltage is greater. STATCOM's ability to regulate the flow of reactive power can increase the network's stability. By optimizing the network's reactive power, the power factor is increased and stabilized up to 0.99. In addition, the system's harmonics never exceed the 5% limit specified by the IEEE 519-1992 standard.

Effectiveness analysis of twelve sectors of DTC based on a newly modified switching table implemented on a wind turbine DFIG system under variable wind velocity

This paper aims to present a novel Direct Torque Control (DTC) strategy based on twelve sectors and a new Modified Switching Table (12-MST-DTC) used to control the Doubly Fed Induction Generator (DFIG) powered by Wind Turbine (WT) and operating under random wind speed. Robustness and fast dynamic performances have made Conventional DTC (C-DTC) popular and widely used, but presents major drawbacks which are high torque and flux ripples. Therefore, the 12-DTC has been proposed in the literature as an improvement but suffers from significant oscillations during super-synchronous operation mode as well as the case of overspeed. The proposed control strategy maintains the performances that offer (C-DTC and 12-DTC) and furthermore minimizes those drawbacks. The newly modified switching table is designed with additional input to cover all WT-DFIG system operation modes (sub-, super- and synchronous modes in addition to the case of over-speed) in order to diminish the flux, torque ripples and THD of the AC-grid injected currents. For optimal operating of variable-speed WT, MPPT algorithm and blade pitch control are invested. Simulation results of the 12-MST-DTC are compared with those of C-DTC and 12-DTC using MATLAB/Simulink environment treating all operation modes of WT-DFIG system and even in case of wind over speeds. The comparison results demonstrate the efficiency of the proposed control strategy in terms of flux/torque ripples and THD reduction, therefore fewer DFIG mechanical vibration and acoustic noise.

Comparative study of magnetic energy storage control techniques for stabilizing wind farm integrated system under different conditions

The Superconducting Magnetic Energy Storage (SMES) device is gaining significance in utility applications, as it can handle high power values with a fast rate of exchanging energy at high efficiency. The large-scale invasion of renewable energy systems into grid give rise to many integration issues. This SMES can handle these issues due to its various features. In this study the role of SMES in tackling stability issues due to presence of wind generation farms is investigated. The Doubly Fed Induction Generators (DFIG) based wind farms are most popular due to their distinctive advantages. Their intermittent output power and sensitivity to the grid disturbances gives rise to stability issues. These problems require innovative and advanced control strategies. Here coupling of SMES with Static Compensator (STATCOM) (STAT-SMES) has been suggested for boosting the transient stability of the hybrid large-scale DFIG and Synchronous Generator (SG) system. For investigation different faults and fast speed wind variation are considered. This coupled controller can compensate the real and reactive powers along with fast damping of oscillations under test conditions of entire system. The chopper link of SMES is controlled using Fuzzy based intelligent system and Proportional Integral (PI) controller. The connection of SMES to STATCOM shows better damping and improvement in all variables. The comparative study among Fuzzy based STAT-SMES, PI based STAT-SMES, only STATCOM, and without any controller is performed using MATLAB/Simulink. The simulation outcomes indicate that proposed Fuzzy based controller is better than other control systems in different test conditions.

ANN Control for Improved Performance of Wind Energy System Connected to Grid

This paper proposes the novel control strategy to improve the power quality injection of wind energy system using Doubly Fed Induction Generator (DFIG) into the grid by implementing artificial neural network. The torque ripple produced in DFIG due to loading by grid tied inverter, which leads to poor power quality injection into the system. Also, these ripples transferred by DC link and causes heating losses and generator phase current distortion. Therefore, this paper modelled ANN based control scheme to reduce the torque ripple content and restrict the transfer of ripple by DC link to improve the outcome of wind energy system while operating in variable conditions. The DFIG system under studied are modelled and simulated in MATLAB SIMULINK to verify the improvement using proposed control strategy. The recent control technique is also simulated for reflecting the effectiveness in the proposed control method. The outcomes obtained are studied and analysed with the existing control scheme to highlight the improvement obtained by proposed control.