Search Results (419)

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

This document focuses on the mitigation of subsynchronous resonance (SSR) in doubly fed induction generators (DFIGs) through the application of an effective solution based on the use of a Static Synchronous Compensator (STATCOM) with fuzzy logic. The STATCOM, a static parallel compensator, improved the stability, quality, and reliability of the power supply in distribution systems by optimizing the response to voltage fluctuations. Combined with fuzzy logic, it provided flexible and efficient control, reducing oscillations arising in the system. Two case studies were carried out in which the DFIG and the STATCOM module with fuzzy logic were implemented in IEEE 13- and IEEE 33-bus systems. Comparative analyses with and without compensation were performed to assess the system’s behavior in response to oscillations generated by the generator, taking voltages as the main variable. The results showed that the fuzzy–PI controlled STATCOM effectively stabilized voltage profiles, mitigating SSR and improving system reliability. In the IEEE 13-bus case, voltage oscillations were reduced by approximately 72% and the bus voltages converged to 0.99–1.01 p.u. within 1.5 s. In the IEEE 33-bus system, the controller achieved a suppression rate of 68%, with voltages restored to 0.98–1.02 p.u. in less than 2 s. These findings demonstrate the efficiency of the proposed fuzzy–PI STATCOM in suppressing subsynchronous oscillations and enhancing stability in DFIG-based networks. Full article

The electromagnetic vibration characteristics of doubly fed induction machines (DFIMs) employed in variable-speed pumped storage units, which must accommodate frequent power response and operational mode transitions, serve as critical indicators for assessing unit safety and stability. Nevertheless, there persists a significant research gap regarding generalized vibration analysis models and comprehensive investigations into their steady-state and dynamic vibration performance. To address this challenge, this study develops a universal analytical model for electromagnetic excitation forces in DFIMs using Maxwell’s stress tensor method, explicitly incorporating operational conditions such as rotor eccentricity and load imbalance. Using a 300 MW DFIM as a case study, we employ a hybrid numerical-analytical approach to examine the detrimental effects of harmonic currents generated by rotor-side converters. Furthermore, we systematically analyze how spatial harmonics induced by mechanical faults and temporal harmonics arising from electrical faults collectively influence the electromagnetic vibration behavior. Experimental validation conducted on a 10 MW DFIM prototype through vibration displacement measurements confirms the efficacy of the proposed analytical framework. Full article

Based on the actual data of a 300 MW doubly-fed variable speed pumped storage units (DFVSPSUs) in China, the reactive power characteristics of both the stator side and the grid-side converter are analyzed, and the reactive power regulation capability of the unit is discussed. First, the power coupling relationship is analyzed, demonstrating that the reactive power-regulation capability is jointly composed of the stator side and the grid-side converter, without direct coupling between them. Next, we determine the doubly-fed induction generator (DFIG) capacity, explaining that the capacity of the DFIG exceeds the rated capacity of the unit. Then, we note that the stator-side reactive power regulation capability is limited by prime mover power, stator current, and rotor current, while the grid-side converter regulation capability is influenced by converter capacity and rotor-side real power. Furthermore, the stator-side, grid-side converter and total reactive power-regulation capabilities of the unit under different water heads and real power conditions are determined. The results demonstrate that fully considering the grid-side converter can increase the unit’s reactive power regulation capability by 12% to 26%. Finally, by comparing the reactive power operating ranges of fixed-speed and variable-speed units, the reactive power advantages of the variable-speed unit are quantified. Full article

This paper introduces a novel strategy for the diagnosis and fault-tolerant control (FTC) of inter-turn short-circuit (ITSC) faults in the stator windings of Doubly Fed Induction Generator (DFIG)-based wind turbines. ITSC faults are among the most common electrical issues in rotating machines: early detection is therefore essential to reduce maintenance costs and prevent severe damage to the wind turbine system. To address this, a Fault Detection and Diagnosis (FDD) approach is proposed to identify and assess the severity of ITSC faults in the stator windings. A state-space model of the DFIG under ITSC fault conditions is first developed in the (d,q) reference frame. Based on this model, an Unknown Input Observer (UIO) structured using Takagi–Sugeno (T-S) fuzzy models is designed to estimate the fault level. To mitigate the impact of the fault and ensure continued operation under degraded conditions, a T-S fuzzy fault-tolerant controller is synthesized. This controller enables natural decoupling and optimal power extraction across a wide range of rotor speed variations. Since the effectiveness of the FTC relies on accurate fault information, a Proportional-Integral Observer (PIO) is employed to estimate the ITSC fault level. The proposed diagnosis and compensation strategy is validated through simulations performed on a 3 kW wind turbine system, demonstrating its efficiency and robustness. Full article

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article

Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy. Full article

In some areas, such as Gansu in China and Texas in the USA, lots of wind power bases are located far away from load centers. Transmitting large amounts of wind power to load centers through long transmission lines will lead to wind turbines being integrated into a weak grid, which decreases the stability limits of wind turbines. To solve this problem, this study investigates the stability limits of a Doubly Fed Induction Generator (DFIG) connected to a weak grid in a DC-link voltage control timescale. To start with, a model of the DFIG in a DC-link voltage control timescale is presented for stability limit analysis, which facilitates profound physical understanding. Through steady-state stability analysis based on sensitivity evaluation, it is found that the critical factor restricting the stability limit of the DFIG connected to a weak grid is ∂P_e/∂ (−i_rd), changing from positive to negative. As ∂P_e/∂ (−i_rd) reaches zero, the system reaches its stability limit. Furthermore, by considering control loop dynamics and grid strength, the stability limit of the DFIG is investigated based on eigenvalue analysis with multiple physical scenarios. The results of root locus analysis show that, when the DFIG is connected to an extremely weak grid, reducing the bandwidth of the PLL or increasing the bandwidth of the AVC with equal damping can increase the stability limit. The aforesaid theoretical analysis is verified through both time domain simulation and physical experiments. Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

Split-shaft wind turbines decouple the turbine’s shaft from the generator’s shaft, enabling several modifications in the drivetrain. One of the significant achievements of a split-shaft drivetrain is the reduction in size of the excitation circuit. The grid-side converter is eliminated, and the rotor-side converter can safely reduce its size to a fraction of a full-size excitation. Therefore, this low-power-rated converter operates at low voltage and handles regular operations well. However, fault conditions may expose weaknesses in the converter and push it to its limits. This paper investigates the effects of the reduced-size rotor-side converter on the voltage ride-through capabilities required from all wind turbines. Four different protection circuits, including the active crowbar, active crowbar along a resistor–inductor circuit (C-RL), series dynamic resistor (SDR), and new-bridge fault current limiter (NBFCL), are employed, and their effects are investigated and compared. Wind turbine controllers are also utilized to reduce the impact of faults on the power electronic converters. One effective method is to store excess energy in the generator’s rotor. The proposed low-voltage ride-through strategies are simulated in MATLAB Simulink (2022b) to validate the results and demonstrate their effectiveness and functionality. Full article

Aiming at the transient overcurrent problem faced by doubly-fed induction generators (DFIGs) during continuous voltage fault ride-through, a segmented control strategy based on the rotor side converter (RSC) is proposed. First, through theoretical analysis of the relationship between stator current and transient induced electromotive force (EMF) in each stage of continuous faults, a feedforward control strategy based on the transient component of stator current is proposed. The observable stator current is extracted for its transient component, which is used as a rotor voltage compensation term to effectively counteract the influence of transient EMF. Meanwhile, a fuzzy control algorithm is introduced during the low voltage ride-through (LVRT) stage to dynamically adjust the virtual resistance value, enhancing the system’s damping characteristics. Studies show that this strategy significantly suppresses rotor current spikes in all stages of voltage ride-through. Finally, simulation results verify that the proposed method improves the ride-through performance of DFIG under continuous voltage faults. Full article

This study proposes an improved super-twisting sliding mode (STSM) control method for a brushless doubly fed induction generator (BDFIG) used in standalone ship shaft power generation systems. Focusing on the problem of the low tracking accuracy of the power winding (PW) voltages caused by the parameter perturbation of BDFIG systems, a mismatched uncertain model of the BDFIG is constructed. Additionally, an improved STSM control method is proposed to address the power load variation and compensate for the mismatched uncertainty through virtual control technology. Based on the direct vector control of the control winding (CW), the proposed method ensured that the voltage amplitude error of the power winding could converge to the equilibrium point rather than the neighborhood. Finally, in the experimental investigation of the BDFIG-based ship shaft independent power system, the dynamic performance in the startup and power load changing conditions were analyzed. The experimental results show that the proposed improved STSM controller has a faster dynamic response and higher steady-state accuracy than the proportional integral control and the linear sliding mode control, with strong robustness to the mismatched uncertainties caused by parameter perturbations. Full article

The Super-Twisting Sliding Mode Controller (STSMC) is regarded as one of the most straightforward and most practical nonlinear control systems, due to its ease of application in industrial systems. Its application helps minimize the chattering problem and significantly improves the resilience of the system. This controller possesses multiple deficiencies and issues, as its use does not promote the expected improvement of systems. To overcome these shortcomings and optimize the efficiency and performance of this technique, a new method is suggested for the super-twisting algorithm (STA). This study proposes and uses a new STA approach, named the fast super-twisting algorithm (FSTA), utilized the conventional IFOC technique to mitigate fluctuations in torque, current, and active power. The results from this suggested the IFOC-FSTA method are compared with those of the traditional SMC and STA methods. The results obtained from this study demonstrate that the suggested method, which is based on FSTA, has outperformed the traditional method in terms of ripple ratio and response dynamics. This demonstrates the robustness of the proposed approach to optimize the generator performance and efficiency in the double-fed induction generator-based wind system. Full article

Doubly Fed Induction Generator (DFIG)-based Wind Energy Systems (WESs) have become increasingly prominent in the global energy sector, owing to their superior efficiency and operational flexibility. Nevertheless, DFIGs are notably vulnerable to fluctuations in the grid, which can result in power quality issues—including voltage swells, sags, harmonic distortion, and flicker—while also posing difficulties in complying with Fault Ride-Through (FRT) standards established by grid regulations. To address the previously mentioned challenges, this paper develops an integrated approach utilizing a Dynamic Voltage Restorer (DVR) in conjunction with a Lithium-ion storage system. The DVR is coupled in series with the WES terminal, while the storage system is coupled in parallel with the DC link of the DFIG through a DC/DC converter, enabling rapid voltage compensation and bidirectional energy exchange. Simulation results for a 2 MW WES employing DFIG modeled in MATLAB/Simulink demonstrate the efficacy of the proposed system. The approach maintains terminal voltage stability, reduces Total Harmonic Distortion (THD) to below 0.73% during voltage sags and below 0.42% during swells, and limits DC-link voltage oscillations within permissible limits. The system also successfully mitigates voltage flicker (THD reduced to 0.41%) and harmonics (THD reduced to 0.4%), ensuring compliance with IEEE Standard 519. These results highlight the proposed system’s ability to enhance both PQ and FRT capabilities, ensuring uninterrupted wind power generation under various grid disturbances. Full article