Control Of Doubly Fed Induction Generator Research Articles

Performance analysis of DFIG support microgrid using GA optimized restricted Boltzmann Machine algorithm

Voltage and reactive power regulation in a deregulated microgrid can be achieved by strategically placing the Static Synchronous Compensator (STATCOM) in coordination with other renewable energy sources, thus ensuring high-end stability and independent control. STATCOM plays a crucial role in effectively addressing power quality issues such as voltage fluctuation and reactive power imbalances caused by the intermittent nature of wind energy conversion systems. To successfully integrate STATCOM into the existing system, it is essential that the control system employed for STATCOM coordination aligns with the Doubly-Fed Induction Generator (DFIG) controller within the microgrid. Therefore, an efficient control algorithm is required in the microgrid, capable of coordinating with the DFIG controller while maintaining system stability. The utilization of a Genetic Algorithm (GA) in calibrating the Restricted Boltzmannn Machine (RBM) can streamline the process of determining optimal hyperparameters for specific tasks, eliminating the need for computationally intensive and time-consuming grid searches or manual tuning. This approach is particularly advantageous when dealing with large datasets within short time durations. In this research, a Simulink model comprising a DFIG-based microgrid and STATCOM has been developed to demonstrate the effectiveness of the proposed control system using RBM in managing STATCOM and facilitating microgrid operations.

Robust Disturbance Rejection Rotor Current Control of Doubly-Fed Induction Generators

Robust Disturbance Rejection Rotor Current Control of Doubly-Fed Induction Generators

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.

A Novel Adaptive Model Predictive Control Strategy for DFIG Wind Turbine With Parameter Variations in Complex Power Systems

In this paper, a novel adaptive model predictive control (MPC) strategy is proposed for doubly fed induction generator (DFIG) wind turbine (WT), which is integrated into a complex power system, in order to improve the power output tracking precision and dynamic performance. Considering the existence of parameter variations in DFIG, an adaptive parameter estimation method is firstly designed. By analysis of stability, the convergence of the parameter estimation algorithm is rigorously proved. Furthermore, the parameter estimation algorithm is effectively integrated into MPC to realize real-time optimal control of DFIG with the adaptive model. To achieve the rotor side converter (RSC) design based on adaptive MPC, the DFIG model is linearized. In addition, a virtual output compensation (VOC) strategy is adopted to alleviate the impact of model linearization errors on the MPC, especially the variation of the model parameter. The newly proposed adaptive MPC-based RSC is capable of greatly improving the tracking performance, meanwhile taking into account the realistic constraints under various operation conditions. The simulation results demonstrate the effectiveness and superiority of the proposed control method.

A robust transient energy function‐based controller for enhancing transient stability of virtual power system

AbstractThis paper offers non‐linear control for enhancing the transient stability in power systems containing a photovoltaic (PV) and doubly fed induction generator (DFIG) by transient energy function (TEF) method and finite‐time observers. At first, the TEF method is utilized to enhance the transient stability in a power system containing a PV, DFIG, and synchronous generator (SG) by constructing a hybrid Lyapunov function. Then, the derivative terms in control are estimated with finite‐time observers. The main contribution of the research is the application of the TEF technique in the transient stability evaluation of a power system consisting of PV, SG, and DFIG. Another innovation of the research is the simultaneous control of DFIG and PV, whose uncertain parameters are estimated with the use of finite‐time observer. The proposed non‐linear control scheme is robust against changes in the configuration of the power system. In order to show the capability of this control scheme, time domain simulations are performed on the IEEE 9‐bus, and the NEW ENGLAND Standard 39‐Bus power system. Using the obtained result, some comparisons are made with the back‐stepping control scheme to clarify the superiority of the proposed method. The comparisons show that the proposed non‐linear control scheme can properly provide extra damping to reduce the first‐swing fluctuations in less time.

Improved Power Control of DFIGs Driven by Wind Turbine under Unbalanced Grid Voltage

AbstractUndesired oscillation components appear in active and reactive powers, electromagnetic torque and DC-link voltage of doubly fed induction generators (DFIGs) connected to unbalanced grid voltage. These components oscillate at double source frequency as a result of negative sequence components in voltage and current. Different direct power control (DPC) techniques were studied in literatures to damp these oscillations. However, these techniques require sequence decomposition process, axes transformation of stator voltage/current and estimation of different power components which complicate the overall control system. This paper presents a simplified DPC of DFIGs in stationary reference frame under normal and unbalanced grid voltage. Decomposition process, axes transformation and compensation power terms are totally eliminated. Vector proportional- integral (VPI) controllers are designed to regulate stator active and reactive powers. The performance of the proposed DPC scheme using VPI and proportional-integral-resonant (PIR) controllers is analyzed and compared under different operating conditions. Bode diagram of open loop and closed loop control using VPI and PIR are studied to illustrate stability, steady state and transient response of the two controllers. Also, the performance of proposed technique and previous DPCs designed in synchronous reference frame is compared to prove the validity of proposed one. The results show that proposed DPC using VPI has superior performance in steady state and transient conditions with simple implementation.

Chaos Control of Doubly-Fed Induction Generator via Delayed Feedback Control

With the increasing wind power penetration, wind farms are directly influencing the power systems, so the need to improve the quality of the system is an open research topic. A Doubly-Fed Induction Generator (DFIG) is often used in wind power systems. However, DFIG has a complex structure and often works in harsh environments, so potential faults may occur. Faults can cause the system to fall into a chaotic working state, which is a harmful phenomenon for DFIG, since it makes the operating quality of the system worse, even leading to system destruction if not fixed on time. This study presents simulations of the chaotic phenomenon that occurs for a DFIG under specific working conditions based on Lyapunov’s exponents. The delay feedback controller is designed, and along with the selection of the appropriate controller parameters, the chaotic phenomenon is quickly eliminated, bringing the system back to stable operation.

Grid-forming control of doubly-fed induction generators based on the rotor flux orientation

The increasing penetration of renewable energies in power systems demands new services from renewable generation plants. System operators are concerned about system stability since renewable generators behave as constant power sources. Therefore, new requirements have been imposed to grid-following generators to improve their contribution to system stability acting as grid-supporting generators. Nevertheless, grid-supporting control can still compromise system stability for high penetration of renewables, and grid-forming control has arised to ensure proper operation. This paper proposes a novel grid-forming control scheme for doubly-fed induction generators, so they behave as real voltage sources. The proposed grid-forming control is based on the rotor flux orientation to a reference axis obtained from the emulation of the synchronous generator swing equation. The rotor flux is oriented to the reference axis by means of a flux controller that also controls the flux magnitude. The flux orientation in turn allows to control the doubly-fed induction generator torque, while the flux magnitude control allows to regulate the generator reactive power or terminal voltage. The proposed control system has been validated through a comprehensive real-time simulation with hardware in the loop, assessing its grid-forming capability. Moreover, small signal analysis has also been performed to assess system stability.

Effect of DFIG control parameters on small signal stability in power systems

The doubly-fed induction generator (DFIG) with virtual inertia control and reactive damping control gives a renewable energy generation system inertia and damping characteristics similar to those of a thermal power plant, and the parameters of the control strategy have a direct impact on the small-signal stability of the system. This paper firstly introduces the operating characteristics and control strategies of DFIG-based damping control and virtual inertia control, establishes a small-signal model of the control-based DFIG integrated interconnected system, and investigates the effects of virtual inertia and reactive damping values on the small-signal stability of the system; then, the maximum damping ratio of the interval oscillation mode in small disturbance analysis is taken as the optimization objective, and the control parameters are the optimization variables. An optimization method of inertia and damping parameters is established for improving the small disturbance stability of the system. The results show that the optimization procedure could improve the damping ratio of the interval oscillation mode while ensuring the system frequency. The effects of virtual inertia and reactive damping values on the small signal stability of the system are investigated, and an optimal allocation model and method for virtual inertia used to improve the small disturbance stability of the system is proposed.

Impact of inertia response control strategy based doubly fed induction generator on frequency stability of power system

While the doubly fed induction generator (DFIG) is widely applied in the new power system, its decoupled control and fluctuation of output power have led to serious problems in frequency stability. Inertia response control (IRC) of DFIG has attracted significant attention, while its impact has not been clearly researched. In this study, a low-order model of DFIG focusing on frequency stability is proposed. Firstly, the simplified model of DFIG considering the IRC is introduced through Taylor series expansion. The frequency response model considering the energy storage system (ESS) is proposed. Taking the rate of change of frequency (RoCoF) and steady-state frequency deviation (SFD) as the constraint, an analytical solution for the maximum proportion of DFIGs is deduced to reflect the impact of IRC on the system frequency stability. Finally, the accuracy of the proposed method and the effectiveness of IRC is verified on the modified IEEE-9 system.

Transient stability increase of multi-machine power system by using SSSC and DFIG control with TEF technique and super twisting differentiator

This paper suggests a nonlinear controller of a Static Synchronous Series Compensator (SSSC) and a doubly-fed induction generator (DFIG) for transient stability enhancement by transient energy function (TEF) technique and super-twisting differentiator in the multi-machine power system. Initially, the TEF approach is employed to damping control and stability enhancement in a multi-machine power system including an SSSC, DFIG, and synchronous generator (SG). Then, the signals of time-derivative in the controller with a super-twisting differentiator are estimated. The significance and novelty of this work are the employs of the TEF method obtained for the multi-machine power system containing DFIG and SG. Another novelty is the use of the DFIG and SSSC simultaneous control whose uncertain parameters are estimated with a super-twisting differentiator. The feature of the nonlinear control approach is robust versus modifications in the topology of the system. Simulation is performed on the IEEE 9-bus system to appraise the operation of the nonlinear controller approach compared to back-stepping and sliding mode control. The simulation results demonstrate that the nonlinear controller approach satisfactorily reduces the first swing of oscillations.

Power Control of a Grid-Connected Doubly Fed Induction Generator Using H∞ Control and Kalman Filter

A new control method for a grid-connected doubly fed induction generator (DFIG) is proposed in this paper, which is robust against parametric uncertainty and measurement noise. In general, the DFIG controllers can be divided into two main groups: the rotor side converter (RSC) and the grid side converter (GSC) controllers. The parameters of a DFIG may deviate from their rated values due to the operating conditions. For this parametric uncertainty, a robust H∞ vector control (VC) is employed using the complex sensitivity approach. The design of the RSC controller has been carried out using the vector control strategy, and instead of proportional-integral (PI) controllers, a designed robust controller is used. One of the steps in vector control is the extraction of the measured currents to be used in the control equations. If the currents are polluted with noise, the system control will be impaired. Thus, using a Kalman filter is suggested to solve this problem. The effectiveness of the proposed method is then investigated using extensive simulations under various conditions. The obtained results confirm the efficient performance and robustness of the presented controller with model and measurement uncertainties.

Sensorless passivity based control of doubly-fed induction generators in variable-speed wind turbine systems based on high gain observer

The current study presents a robust sensorless control using passivity based control (PBC) combined with high gain observer (HGO). The proposed controller is applied to control the generated doubly-fed induction generator (DFIG) active and reactive power installed on a variable speed wind energy conversion system. The control objective is used to regulate independently the DFIG stator active and reactive power, which are decoupled by using the field oriented control technique. Additionally, this process leads to reduce the cost of control scheme by eliminating the speed sensor. Firstly, the DFIG is modeled under the port controlled Hamiltonian (PCH) model, as well as the method of simultaneous injection damping. Then, the DFIG is further modeled by assignment passivity based on the simultaneous injection damping and assignment (SIDA-PBC) control of the obtained model under such conditions and a comparison with the fuzzy sliding mode controller is carried out. Furthermore, the HGO is selected in order to estimate the rotor position and the speed from the measurement of the DFIG currents and voltages, and compared with fuzzy sliding mode observer. For testing the proposed control scheme performance, a 1.5 MW DFIG system is developed and simulated using MATLAB/Simulink. The obtained results demonstrate the effectiveness of the proposed control scheme in the presence of various DFIG parameters variation. Additionally, the control objective is achieved without speed sensor.

Frequency regulation control strategy of wind farms based on temporal and spatial uncertainty

The Frequency regulation capability of a double-fed induction generator (DFIG) in a large wind farm is influenced by its operating state and installation position, as well as the wind speed (size and direction) at which it is located, resulting in temporal and spatial uncertainties. First, to simplify control difficulty and optimize frequency regulation increment allocation, model predictive control(MPC) and k-means clustering method are used for spatial optimal grouping of DFIG in wind farms based on the uncertainties of DFIG installation location and wind direction. Second, a timing coordinated control strategy is proposed based on the uncertainties of the DFIG operation state and wind speed. The strategy allows the DFIG to enter and exit the frequency regulation system based on wind speed, active power increment, and sustainable time and then reduces the negative effect of the DFIG exiting the frequency regulation system. Finally, based on temporal and spatial optimization, a coordinated control strategy based on temporal and spatial uncertainties is proposed to optimize the overall frequency regulation capability of wind farms. Simulation examples show that the time–space coordinated control strategy treats each group as an independent DFIG control, which not only simplifies the control problem but also fully utilizes the DFIG frequency regulation capacity and effectively improves the system frequency characteristics.

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

Novel non‐linear control of DFIG and UPFC for transient stability increment of power system

This paper offers novel non-linear control of a doubly fed induction generator (DFIG) and unified power flow controller (UPFC) for transient stability increment with transient energy function (TEF) and sliding mode observer in a power system. First, the TEF technique is considered for the damping control in a power system with a synchronous generator (SG), DFIG, and UPFC, and then the controller time-derivative signals are estimated by a second-order sliding mode observer. The importance of the issue is in the employ of a complete UPFC model. Also, a one-axis model is used for DFIG that is similar to the SG model. Another characteristic of the novel non-linear technique is robust versus system topology changes and variable time delay of the control signals. To evaluate the performance of the novel non-linear control method for increment transient stability, simulation studies are performed on a two-machine connected to an infinite bus (TMIB), the IEEE 9-bus, and the New England Standard 39-bus power system. The results determine that the novel non-linear control method decreases the first swing of fluctuations admissibly and enhancement stability margins considerably.

The impact of DFIG control schemes on the negative-sequence based differential protection

Negative-sequence currents at the transmission line terminals are widely adopted in differential protection to achieve excellent sensitivity. The underlying principle is that the traditional power sources (synchronous generators) can be treated as inductive reactance in the negative-sequence network. However, for the transmission lines connected to Doubly-Fed Induction Generator (DFIG) based wind parks (WPs), the negative-sequence currents are controlled by fault-ride-through (FRT) solutions. This paper first analyzes the phasor characteristics between negative-sequence voltage and current of a DFIG-based WP under three typical FRT solutions, and then evaluates their impact on the dynamic performance of negative-sequence differential protection elements (87LQ). The analysis indicates that a certain FRT control strategy would make DFIG-based WP provide capacitive current into the negative-sequence network. This impacts the sensitivity and even results in maloperation as shown for the first time in this work. The analytical results are validated with detailed time domain models and simulations in Simulink.

High-performance MPPT control of DFIG with optimized flux reference in presence of nonlinear magnetic characteristic

In recent years, an expansive research exertion has been devoted to the control for aeorogenerators conforming of a Doubly Fed Induction Generator (DFIG) coupled to a wind turbine. Most preceding controllers based on standard models (whose magnetic induction characteristic is considered linear and iron losses have not been taken into account). Since such a magnetic characteristic is nonlinear in practice, this work develops a variable speed control strategy on the basis of a DFIG model in presence of nonlinear magnetic characteristic and iron losses. The main contribution of this paper is to present a high-performance maximum power point (MPPT) control of DFIG based on rotor flux optimization, designed to reach an optimal operational conditions. Of the foregoing, the control goal is twofold: i) minimization of the stator and rotor currents circulating by controlling the rotor flux for a maximum electromagnetic torque; ii) captured the maximum wind power to insure the attainment of MPPT. The performances of the proposed command has been studied theoretically and validated by simulation studies with Matlab/Simulink.

Stability analysis of doubly-fed wind generation systems under weak power grid based on virtual synchronous control combined with adaptive robust control

Doubly-fed induction generator (DFIG) of wind generation system is generally located at the end of the distribution power-grid. Under the weak power grid, the grid connection of wind turbines is unstable, and the current quality is poor. Based on DFIG output impedance model, a stability analysis method combined with adaptive control method is proposed in this paper. The DFIG impedance model with power outer-loop is derived based on the virtual synchronous control of DFIG by using adaptive robust control method. Using generalized Nyquist criterion, the impacts of different power grid conditions and control parameters on the system stable operation under virtual synchronous control are analyzed and verified. The proposed control method in this paper is verified by experiments. The proposed control method improves the quality of grid-connected current.

Enhanced Control of DFIG Based Wind Energy Conversion System Under Unbalanced Grid Voltages Using Mixed Generalized Integrator

The operation of a wind energy conversion system based on a doubly fed induction generator (DFIG) is found to be extremely sensitive to unbalanced grid voltages. In this context, this article presents an enhanced control strategy for a DFIG operating under unbalanced grid voltages, using a mixed generalized integrator (MGI). The MGI is a hybrid form of the second and third-order generalized integrators and is employed for positive and negative sequence calculation. The MGI-based enhanced control strategy utilizes the hysteresis band control of rotor currents and grid currents, to generate switching sequences for rotor side converter and grid side converter, respectively. In addition to superior dynamic tracking of reference currents, this scheme eliminates the need for implementation and careful tuning of eight distinct proportional-integral inner current regulators, as is the case with conventional control strategy for the DFIG operating at unbalanced grid voltages. Furthermore, MGI improves the grid synchronization in DFIG system amidst the presence of dc offset and unbalance in the supply voltages. The overall coordinated control strategy is designed to realize seven separate control objectives, and its effectiveness is verified through simulation and experimental results.