To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration.

Advanced Control and Integration of Fuel Cell With Doubly Fed Induction Generators for Sustainable Power Generation

Reliable detection of the rotor winding faults in the doubly fed induction generator (DFIG) is crucial for the resilience of the variable speed energy systems. High-resistance connection (HRC) and inter-turn short circuit (ITSC) faults cause current distortions that are remarkably similar, and the rapid rotor side dynamics and the DFIG multimode operation ability also make fault diagnosis more difficult. This paper proposes a three-layer diagnostic framework named ZSC-CASI-CADI which leverages three-phase rotor currents in conjunction with rotor zero-sequence current (ZSC) for comprehensive rotor winding fault diagnosis. Fault detection is realized through ZSC magnitude and the Cosine Angle Spread Indicator (CASI) enables the strong discrimination between HRC and ITSC faults using the dispersion of rotor current phasors from the ZSC reference. Fault localization is achieved using the Current Angle Difference Indicator (CADI), which determines the faulty rotor phase through the angular deviations in rotor currents from the ZSC. The methodology is verified with extensive simulation results to demonstrate the accurate, real-time fault detection, discrimination, and localization of DFIG rotor winding faults under different load and rotor speed conditions including sub-synchronous and super-synchronous modes. The results show that the proposed framework provides a light and effective solution for rotor winding fault monitoring of the DFIG systems.

This paper offers an overview of control in electrical machines. The primary aim of this paper is to determine the optimal control method for most types of electrical machines. First of all, the principle of controlling electrical machines in general has been explained, after that, scalar control and vector control have been studied. Direct and indirect vector control have been studied and compared, in addition to determining the advantages and disadvantages of each of them. This paper has focused on studying the vector control of a Doubly Fed Induction Generator (DFIG), as it is one of the most widely used generators in wind energy. The control of current, power and speed from the rotor side has been studied by building a set of equations that represent the mathematical model of Doubly Fed Induction Generator (DFIG). As for the grid side, the method of regulating the reactive power between Doubly Fed Induction Generator (DFIG), and the grid has been explained. The results are discussed, along with a simplified comparison between scalar and vector control. The paper also includes a simplified comparison between generator types, identifying the advantages and disadvantages of each.

Over recent decades, especially in the transmission network, the share of electricity produced by wind sources has significantly increased. In order to enhance the efficiency of the power system, it is crucial to have precise and comprehensive mathematical modeling of the generator for wind turbine systems. Typically, this generator is a doubly-fed induction generator, as its use of partial converters and induction machines makes it more economically successful compared to alternative technologies. In this paper, a novel approach has been developed for the modeling and analysis of doubly-fed induction generators. This thorough approach takes into account the derivation of the neutral voltage of the doubly-fed induction generator. This innovative approach has successfully extracted the fundamental harmonic of the stator currents and voltages with precision under normal operating conditions within a reasonable simulation time. It has been demonstrated that under normal operating conditions, a typical operation is achieved. It is characterized by a balanced stator voltage, current, flux, and fundamental harmonic through the stator variables, which correspond to the supply frequency. This fundamental harmonic has been suggested as a means of monitoring the generator during normal operating conditions. Mathematics modeling and simulation are conducted using MATLAB software. The validity and dependability of this method for analyzing and modeling doubly-fed induction generators are confirmed by the consistency and strong correlation between experimental and simulation results.

This paper presents a procedure for calculating switching overvoltage on the main circuit breaker of a medium-voltage (MV) cable feeder, to which induction and/or synchronous generators are connected, during a three-phase short circuit. When a fault occurs, the feeder is disconnected by the circuit breaker located at its beginning. After the set operating time of the relay protection, since islanded operation is not permitted, the connected distributed generators will also be disconnected. It is shown that, during the period when the network is disconnected while the generators remain connected, the overvoltage factor reaches values between 2.2 and 2.5, depending on the types of generators and the location of points of common coupling. The individual transient responses of the distribution network and the mentioned types of distributed generators differ significantly. Using the superposition theorem, the calculation of switching overvoltage is demonstrated with elementary computer assistance.

The growing share of distributed energy resources in the power system increases the number of power quality issues. The variable nature of their generation contributes to voltage fluctuations. This paper proposes a method for compensating voltage fluctuations utilising reactive power generated by a doubly fed induction generator (DFIG). The proposed method was first evaluated using a simulation model developed in the Matlab Simulink R2025a environment and subsequently validated experimentally under laboratory conditions. The results obtained are highly satisfactory, with the compensation time in laboratory tests not exceeding 500 ms. Since DFIGs are used in approximately 50% of wind power plants and the implementation of the proposed approach does not require additional hardware—only modifications to the generator control software—the method appears highly promising. It offers the possibility of rapid deployment without incurring significant costs.

During voltage dips, wind turbines must remain connected to the electrical grid and contribute to voltage stabilization. This study analyzes the impact of voltage dips arising from grid faults on Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECSs). This paper presents a review of the technical regulations for integrating the Algerian electricity grid with the Low Voltage Ride Through (LVRT) system, along with specific requirements for renewable power generation installations. Additionally, the modeling and control strategy of DFIG based WECS has been outlined. Voltage dips can induce excessive currents that threaten the DFIG rotor and may cause harmful peak oscillations in the DC-link voltage, and can lead to turbine speed increase due to the sudden imbalance between the mechanical input torque and the reduced electromagnetic torque. To counter this, a modified vector control and crowbar protection mechanism were integrated. Its role is to mitigate these risks, thereby ensuring the system remains stable and operational through grid faults. The proposed system successfully meets the stringent Algerian LVRT requirements, with voltage dipping to zero for 0.3 s and recovering gradually. Simulations confirm that rotor and stator currents remain within safe limits (peak rotor current at 0.93 pu, and peak stator current at 1.36 pu). The DC-link voltage, despite a transient rise due to the continued power conversion from the rotor-side converter during the grid fault, was effectively stabilized and maintained within safe operating margins (with less than 14% overshoot). This stability was achieved as the crowbar ensured power balance by managing active and reactive power. Notably, the turbine rotor speed demonstrated stability, peaking at 1.28 pu within mechanical limits.

This paper presents a robust fractional-order neural-modified sliding mode control (FONMSMC) strategy for enhancing the performance of dual-rotor wind turbines (DRWTs) driven by doubly-fed induction generators (DFIGs). Conventional direct power control methods often exhibit unstable power output, high electrical noise, and limited adaptability under variable wind conditions. The proposed FONMSMC integrates fractional calculus for precise dynamic tuning, neural networks for adaptive adjustment, and a modified sliding mode control framework to improve robustness, combined with pulse-width modulation for efficient power conversion. Simulation results demonstrate significant performance improvements, including reductions of up to 94% in active power steady-state error, 87.2% in active power fluctuations, and 71.79% in total harmonic distortion. The controller also maintains stable operation across a wide range of wind speeds, ensuring enhanced grid stability and reduced mechanical stress. The proposed method offers a reliable and efficient control solution for DFIG-based DRWT systems, contributing to improved sustainability and robustness in modern wind energy applications.

Wind energy systems using doubly fed induction generators (DFIGs) rely on vector-oriented control (VOC) to achieve decoupled regulation of active and reactive power. Conventional VOC strategies typically employ proportional-integral (PI) controllers for rotor current control; however, these controllers struggle under nonlinear and variable operating conditions, such as wind speed fluctuations, leading to degraded dynamic performance and power quality. To overcome these limitations, this paper proposes an intelligent VOC approach based on the M5-Pruned model tree (M5P) algorithm, complemented by a comparative study with fuzzy logic controllers (FLC). The M5P-based controller introduces a data-driven mechanism that adapts to system dynamics, ensuring improved accuracy and robustness compared to traditional PI and FLC methods. A detailed MATLAB/Simulink simulation of a grid-connected wind turbine with DFIG evaluates the controllers under varying wind profiles. Results demonstrate that the proposed M5P strategy significantly reduces overshoot and settling time, enhances power regulation, and improves overall system stability. These findings highlight the advantages of machine learning-based control in addressing the shortcomings of conventional VOC techniques for renewable energy applications.

ABSTRACT The conventional direct field‐oriented control (DFOC) strategy using proportional–integral (PI) regulators for managing the energy of a doubly fed induction generator (DFIG) in wind turbine systems often proves inadequate due to the PI controller's sensitivity to parameter variations. Additionally, it tends to produce lower‐quality energy output. To address these shortcomings, this study proposes a novel control strategy that combines two fractional‐order controllers: a fractional‐order proportional‐derivative (FOPD) regulator and a fractional‐order integral dual‐derivative (FOIDD) regulator. These regulators are valued for their simplicity, low cost, and ease of implementation. The hybrid FOPD–FOIDD approach aims to enhance the performance and robustness of the traditional DFOC‐PI control applied to DFIG‐based wind turbine systems, enabling improved power regulation and dynamic response. To further optimize the designed control system, Particle Swarm Optimization is used to fine‐tune the controller parameters, ensuring efficient and stable power generation under varying and dynamic wind conditions. The new regulator replaces the classical PI in the DFOC scheme for the rotor‐side converter of the DFIG. The design and simulations were realized in MATLAB, and results were rigorously compared with those of the DFOC‐PI system under diverse operating conditions, including variations in active power reference, rapid wind speed changes, and parameter uncertainties. The comparative analysis demonstrates that the proposed FOPD–FOIDD controller significantly outperforms the DFOC‐PI. Simulation results show major improvements in dynamic performance, including reductions in current harmonic distortion by up to 87.55% and 14.14%, and substantial decreases in active power, torque, and reactive power ripples—by 93.18%, 92.42%, and 74.99%, respectively. Overall, the new control strategy exhibits superior robustness and stability, maintaining high‐quality power generation despite unpredictable variations in generator parameters.

Recently, the installed generation capacity of wind energy has expanded significantly, and the doubly fed induction generator (DFIG) has gained a prominent position amongst wind generators owing to its superior performance. It is extremely vital to enhance the low-voltage ride-through (LVRT) capability for the wind turbine DFIG system because the DFIG is very sensitive to faults in the electrical grid. The major concept of LVRT is to keep the DFIG connected to the electrical grid in the case of an occurrence of grid voltage sags. The currents of rotor and DC-bus voltage rise during voltage dips, resulting in damage to the power electronic converters and the windings of the rotor. There are many protection approaches that deal with LVRT capability for the wind turbine DFIG system. A popular approach for DFIG protection is the crowbar technique. The resistance of the crowbar must be precisely chosen owing to its impact on both the currents of the rotor and DC-bus voltage, while also ensuring that the rotor speed does not exceed its maximum limit. Therefore, this paper aims to obtain the optimal values of crowbar resistance to minimize the crowbar energy losses and ensure stable DFIG operation during grid voltage dips. A recent optimization technique, the Starfish Optimization (SFO) algorithm, was used for cropping the optimal crowbar resistance for improving LVRT capability. To validate the accuracy of the results, the SFO results were compared to the well-known optimization algorithm, particle swarm optimizer (PSO). The performance of the wind turbine DFIG system was investigated by using Matlab/Simulink at a rated wind speed of 13 m/s. The results demonstrated that the increases in DC-link voltage and rotor speed were reduced by 42.5% and 45.8%, respectively.

This research focuses on the development of a robust modified superior-order sliding mode controller (MSOSMC) based on the neural super-twisting algorithm (NSTA) for application in wind turbine conversion systems (WTCS) employing the doubly powered induction generator (DPIG). The study is conducted under real conditions in the Adrar region of Algeria. The optimal controller parameters are determined using the grey wolf optimizer (GWO) and are compared with results obtained using particle swarm optimization (PSO). In the DPIG system, the stator is directly connected to the main grid, while the rotor is linked to the grid through a back-to-back inverter. The research addresses the challenges and constraints associated with using individual superior-order sliding mode strategy, particularly the ripple caused by the discontinuous low (sign section) of conventional SOSMC, which can affect energy quality, and the materials used in wind conversion systems. The neural super-twisting algorithm (NSTA) integrated into the SOSMC is employed to maximize wind energy extraction and mitigate ripple issues. Furthermore, it contributes to improving power supply quality, especially in the presence of parametric variations. Simulation tests validate the effectiveness of the proposed approach (MSOSM/NSTA) using GWO, showcasing enhanced robustness and performance compared to conventional SOSMC and sliding mode first order (SMC1), particularly in addressing ripple problems. The study also assesses the system's response to wind speed fluctuations and its high robustness against changes in machine parameters.

The Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) has gained significant attention due to its capability to operate efficiently over a wide range of wind speeds and in various modes. To enhance the performance and reliability of such systems, advanced control strategies are essential. This study introduces a Hybrid Fennec Fox–Sand Cat Optimization Algorithm (HFFSCOA) integrated with a Cascaded Adaptive Neuro-Fuzzy Inference System (ANFIS) for Maximum Power Point Tracking (MPPT) control of DFIG-based WECS. The proposed hybrid algorithm adaptively tunes ANFIS membership functions and parameters, improving its learning capability and ensuring accurate maximum power tracking with minimal oscillations. The coordinated control of the Rotor Side Converter (RSC) and Grid Side Converter (GSC) maintains a stable DC-link voltage and facilitates smooth power delivery to the grid. Moreover, d–q transformation is employed for harmonic suppression, enhancing overall power quality and ensuring compliance with grid standards. Simulation results in MATLAB/Simulink demonstrate the proposed controller’s effectiveness in minimizing active (Ps) and reactive (Qs) power fluctuations while achieving a remarkably low total harmonic distortion (THD) of 0.09%. Consequently, the proposed HFFSCOA-Cascaded ANFIS MPPT offers an intelligent, efficient, and scalable solution for sustainable wind energy systems seamlessly integrated into modern smart grids.

This paper proposes a new fractional-order type-2 fuzzy logic control (FO-T2FLC) technique for maximum power point tracking (MPPT) in a wind turbine system based on doubly fed induction generators (DFIGs). Type-1 fuzzy logic control (T1FLC) and traditional proportional-integral (PI) techniques have limitations in terms of accuracy, reaction time, and resilience under fluctuating wind conditions. The suggested FO-T2FLC combines type-2 fuzzy logic with fractional calculus to enhance dynamic performance, minimize steady-state error, and increase system flexibility, all without requiring a precise mathematical description of the system. The MATLAB simulations are used to evaluate the control strategy under step, random, and fault-like wind speed fluctuations. Comparative analyses show that FO-T2FLC outperforms PI and T1FLC by more than 99% in terms of performance indices, including Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Squared Error (ISE), and Integral Time Squared Error (ITSE). It also minimizes tracking error and converges to the optimal tip speed ratio more quickly. The results demonstrate that the FO-T2FLC-based MPPT approach significantly enhances the robustness, stability, and efficiency of DFIG wind energy conversion systems, making it a viable option for contemporary wind power applications.

This study presents the development and experimental validation of a novel wind turbine emulator (WTE) based on a doubly fed induction generator (DFIG). The proposed architecture employs an induction motor (IM) driven by a variable frequency drive (VFD) to emulate wind turbine dynamics, offering a cost-effective and low-maintenance alternative to traditional DC motor-based systems. The contribution of this work lies, therefore, not in the hardware topology itself, but in the complete real-time software implementation of the control system using C language and RTLib, which enables higher sampling rates, faster PWM updates, and improved execution reliability compared with standard Simulink/RTI approaches. The proposed control structure integrates tip–speed ratio (TSR)-based maximum power point tracking (MPPT) with flux-oriented vector control of the DFIG, fully coded in C to provide optimized real-time performance. Experimental results confirm the emulator’s ability to accurately replicate real wind turbine behavior under varying wind conditions. The test bench demonstrates fast dynamic response, with rotor currents settling in 11–18 ms, and active/reactive powers stabilizing within 25–30 ms. Overshoots remain below 10%, and steady-state errors are limited to ±1 A for currents and ±100 W/±50 VAR for powers, ensuring precise power regulation. The speed tracking error is approximately 0.61 rad/s, validating the system’s ability to follow dynamic references with high accuracy. Additionally, effective decoupling between active and reactive loops is achieved, with minimal cross-coupling during step changes.

The reliability of wind turbines largely depends on the ability to detect electrical and mechanical faults under variable operating conditions. This paper applies the Non-steady-state Harmonic Order Tracking Analysis (NsHOTA) method to the diagnosis of doubly-fed induction generators (DFIGs) in real wind turbines. Unlike other steady-state and transient techniques, NsHota stabilizes and enhances fault components in any operating regime, allowing for more in-depth analysis. Therefore, this method enables highly accurate fault diagnosis, allowing the measurement and analysis of small degradations over time. The method is validated using eight months of field data from an 850 kW DFIG previously diagnosed with mixed eccentricity. The results demonstrate that NsHOTA improves the consistency and quality of fault feature extraction, reduces background noise, and avoids false negatives under steady and non-steady regimes. In the real data test, NsHOTA is also compared with the steady-state HOTA (SsHOTA) method. These findings confirm the robustness of NsHOTA for real-world wind turbine condition monitoring and highlight its potential integration into predictive maintenance systems.

Aanalysis of the impact of grid-forming doubly-fed induction generator parameters on transient stability and Small-signal stability

A novel algorithm for fault diagnosis of induction generators in wind power systems utilizing stator current signal crossing and finite element modeling

This paper presents a direct power control (DPC) method for a doubly-fed induction generator (DFIG) used in variable-speed wind power systems, combining sliding mode control (SMC) with space vector modulation (SVM). The proposed SMC-based DPC with SVM (SMC-DPC_SVM) achieves decoupled power control through flux orientation, enhancing performance through the robustness of SMC and the precision of SVM. Simulation results demonstrate the effectiveness of this control strategy. The conventional direct power control (C-DPC) approach delivers fast and robust power response, and a comparative analysis between C-DPC and the proposed SMC-DPC_SVM strategy highlights the advantages of the latter. Robustness was evaluated under varying machine parameters, confirming system stability. The proposed control method was implemented and validated using MATLAB/Simulink, achieving a total harmonic distortion (THD) of less than 5%, indicating high-quality power delivery to the electrical grid.