Search Results (143)

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

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters. Full article

With the large-scale integration of wind power, transient stability issues in power systems have become increasingly prominent, among which the impact of the active power recovery rate of wind turbines on system stability cannot be ignored. This paper establishes a sensitivity analytical model between the transient stability index of the system and the active power recovery rate of doubly fed induction generators (DFIGs), revealing the influence of active power recovery rate on system stability. First, the trajectory analysis method is adopted as the transient stability assessment approach, proposing a stability index incorporating accelerating power and transient potential energy. Analytical sensitivity models for synchronous generator accelerating power and transient potential energy to the active power recovery rate of wind turbines are derived in a simplified system. Second, a sensitivity model of the stability margin index to the active power recovery rate is constructed to analyze the influence patterns of the active power recovery rate, initial active power output of wind turbines, and fault duration time on system stability. This research demonstrates that: accelerating the active power recovery rate can restore power balance more quickly but it reduces the rate of transient potential energy variation and delays the peak response of potential energy, thereby decreasing the stability margin; higher initial active power output of wind turbines suppresses the oscillation amplitude of synchronous generators but increases the risk of power imbalance; and prolonged fault duration exacerbates transient energy accumulation and significantly degrades system stability. Additionally, for each 0.1 p.u./s increase in the active power recovery rate of the wind turbine, the absolute value of the stability index of the synchronous machine in the single-machine system decreases by approximately 0.5–1.0, while the sensitivity decreases by approximately 0.01–0.02 s⁻¹. In the multi-machine system, the absolute value of the stability index of the critical machine decreases by approximately 5–10, and the sensitivity decreases by approximately 0.5–1.0 s⁻¹. Full article

The sub-synchronous oscillation (SSO) characteristics and suppression strategies of a hybrid system comprising doubly fed induction generator (DFIG)-based wind turbines and synchronous pumped storage units connected to the power grid via series-compensated transmission lines are analyzed. A modular modeling approach is used to construct a detailed system model, including the wind turbine shaft system, DFIG, converter control system, synchronous machine, excitation system, power system stabilizer (PSS), and series-compensated transmission lines. Eigenvalue calculation-based small-signal stability analysis is conducted to identify the dominant oscillation modes. Suppression measures are also developed using relative participation analysis, and simulations are carried out to validate the accuracy of the model and analysis method. The analysis results indicate that the SSO phenomenon is primarily influenced by the electrical state variables of the DFIG system, while the impact of the state variables of the synchronous machine is relatively minor. When the level of series compensation in the system increases, SSO is significantly exacerbated. To address this issue, a sub-synchronous damping controller (SSDC) is incorporated on the rotor side of the DFIG. The results demonstrate that this method effectively mitigates the SSO and significantly enhances the system’s robustness against disturbances. Furthermore, a simplified modeling approach is proposed based on relative participation analysis. This method neglects the dynamic characteristics of the synchronous machine while considering its impact on the steady-state impedance and initial conditions of the model. These findings provide theoretical guidance and practical insights for addressing and mitigating SSO issues in hybrid renewable energy systems composed of DFIGs and synchronous machines. Full article

Power systems are currently undergoing a transition from centralized synchronous generators to decentralized non-synchronous generators that rely on renewable energy sources. This shift poses a challenge to system operators, as the high penetration levels of renewable energy introduce variability and changes in the physics of power systems. Load-frequency control is one of the biggest challenges faced by electrical grids, especially with increased wind energy penetration in recent years. The inertial controller is one of the methods used to support system frequency in variable-speed wind turbines. In this study, a proportional resonant (PR) controller was added to an inertial controller to achieve better frequency regulation by controlling the active power of the doubly fed induction generator (DFIG). First, the impact of the PR controller parameters on the frequency deviation, overshoot, settling time, and system stability was investigated to identify the optimal values that achieved the lowest frequency deviation while maintaining system stability. Second, the performance of the proposed method was compared that of the traditional method under different load perturbations. The results prove that improperly determining the proportional gain of the PR controller negatively affects system stability and frequency deviation. In addition, the results validate the hypothesis that the proposed method would provide fast frequency support for all the studied cases. The analysis and simulation of these scenarios were performed using the MATLAB/SIMULINK program. Full article

The inherent complexity of wind energy systems has necessitated the development of sophisticated control methodologies to optimize operational efficiency. Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of energy conversion. This study focuses on the critical generator control block, where precise power management is essential to maintaining system stability and preventing operational disruptions. This research introduces an innovative ANN-based Direct Power Control (DPC) approach for a Doubly fed induction generator (DFIG) integrated into a wind power system, introducing a dual-MLP approach for precise power regulation. The proposed DPC-ANN controller proved effective in mitigating current ripples and achieving a near-unity power factor, indicating substantial improvement in power quality. Moreover, the spectrum harmonic analysis revealed that the controller yielded the lowest stator current total harmonic distortion of 1.29%, significantly outperforming traditional DPC-PI (2.76%) and DPC-Classic (2.24%) approaches. The proposed technique was rigorously implemented and validated using a real-time simulator (OPAL-RT) and MATLAB/Simulink (2020–2022) environment, specifically tested under a step wind profile. The real-time experimental validation highlights the practical applicability of this approach, bridging the gap between theoretical ANN-based control and real-world wind energy system implementation. These findings reinforce the potential of intelligent control strategies for optimizing renewable energy technologies, paving the way for more efficient and adaptive wind turbine control solutions. Full article

Wind energy is essential for promoting sustainability and renewable power solutions. However, ensuring stability and consistent performance in DFIG-based wind turbine systems (WTSs) remains challenging due to rapid wind speed variations, grid disturbances, and parameter uncertainties. These fluctuations result in power instability, increased overshoot, and prolonged settling times, negatively impacting grid compliance and system efficiency. Conventional proportional-integral (PI) controllers are simple and effective in steady-state conditions, but they lack adaptability in dynamic situations. Similarly, artificial intelligence (AI)-based controllers, such as fuzzy logic controllers (FLCs) and artificial neural networks (ANNs), improve adaptability but suffer from high computational demands and training complexity. To address these limitations, this paper presents a hybrid adaptive neuro-fuzzy inference system (ANFIS)-PI controller for DFIG-based WTS. The proposed controller integrates fuzzy logic adaptability with neural network-based learning, allowing real-time optimization of control parameters. Implemented within the rotor-side converter (RSC) and grid-side converter (GSC), ANFIS enhances reactive power management, grid compliance, and overall system stability. The system was tested under a step wind speed signal varying from 10 m/s to 12 m/s to evaluate its robustness. The simulation results confirmed that the ANFIS-PI controller significantly improved performance compared with the conventional PI controller. Specifically, it reduced rotor speed overshoot by 3%, torque overshoot by 12.5%, active power overshoot by 2%, and DC link voltage overshoot by 20%. Additionally, the ANFIS-PI controller shortened settling time by 50% for rotor speed, by 25% for torque, by 33% for active power, and by 16.7% for DC link voltage, ensuring faster stabilization, enhanced dynamic response, and greater efficiency. These improvements establish the ANFIS-PI controller as an advanced, computationally efficient, and scalable solution for enhancing the reliability of DFIG-based WTS, facilitating seamless integration of wind energy into modern power grids. Full article

Wind power output fluctuations, driven by variable wind speeds, create significant challenges for grid stability and the efficient use of wind turbines, particularly in high-wind-penetration areas. This study proposes a combined approach utilizing an ultra-capacitor energy storage system and fuzzy-control-based pitch angle adjustment to address these challenges. The fuzzy control system dynamically responds to wind speed variations, optimizing energy capture while minimizing mechanical stress on turbine components, and the ultra-capacitor provides instantaneous buffering of power surpluses and deficits. Simulations conducted on a 50 kW DFIG wind turbine powering a 23 kW load demonstrated a substantial reduction in power fluctuations by a factor of 3.747, decreasing the power fluctuation reduction scale from 13.04% to 3.48%. These results highlight the effectiveness of the proposed system in improving the stability, reliability, and quality of wind energy, thereby advancing the broader adoption of renewable energy and contributing to sustainable energy solutions. Full article

The operation and modelling of doubly fed induction generators (DFIGs) are quite different in grid-connected and stand-alone operated wind energy conversion systems (WECSs). Researchers usually simulate DFIGs in these operations using the pre-built models provided in commercial software, which are built using complex modeling techniques that most researchers in the field are unfamiliar with. In this paper, a simple and easy-to-use modeling approach based on the basic dynamic voltage equations of an induction machine (IM) is proposed to provide a more physical and practical understanding of the dynamic behavior of DFIGs, considering the difference between stand-alone and grid-connected operations. The basic theory and various dynamic models of DFIGs are reviewed and discussed to clarify the complexity of using alternative reference frame coordinates and various state variables in these models. A generic fifth-order DFIG model that is defined in an arbitrary general reference coordinate frame is considered. It is a flux-based model that allows for change in the parameters of the DFIG online and can be used only for grid-connected operations under control. In addition, this model is expanded to be used for stand-alone operation, but can also be used for grid-connected mode operation. The stand-alone model consists of a hybrid modeling approach and more closely resembles the real structure of a stand-alone DFIG system. The modeling technique used for the stand-alone DFIG provides a practical, non-mathematical way to solve the challenge of defining the dynamic equation of the stator voltage when different sizes and types of loads are connected to the stator. Many technical research problems and critical events that are challenging in DFIG-based WECSs can be studied using the proposed simulation models. As pioneering examples, several effective simulations are carried out, aiming to provide new researchers in this field with a more practical, in-depth, and intuitive understanding of the theory and operating principle of DFIGs in both stand-alone and grid-connected operations. The accuracy of the proposed stand-alone model is demonstrated by comparative simulation tests performed in parallel operation with two other pre-built models with the same conditions and power size. Furthermore, both proposed models are validated by simulating them for two different-sized DFIGs of 15 kW and 2 MW. In addition, a real experiment is conducted for the current controlled operation of a stand-alone DFIG using the introduced small-sized laboratory hardware setup. The results obtained through simulations and experiment are presented and discussed. Full article

This paper investigates the stability mechanisms and suppression strategies for sub-synchronous oscillations (SSOs) in hybrid wind farms (HWFs) consisting of doubly-fed induction generator (DFIG) and permanent magnet synchronous generator (PMSG)-based wind turbines. To address the challenges in mitigating SSOs due to the complex interactions between the generators in hybrid wind farms, as well as external and parameters disturbances, a state-space model of the HWF is developed to capture the impact of external disturbances and parameter uncertainties on system dynamics. Through eigenvalue and participation factor analyses, this paper examines the effects of uncertainties in voltage control parameters, grid line series compensation, and the fluctuating power outputs of DFIGs and PMSGs on SSO behavior. A robust SSO suppression controller based on H_∞ theory is proposed, demonstrating a substantial reduction in harmonic distortion and improved settling time compared to conventional control strategies under varying disturbances. The simulation results show that the proposed controller significantly enhances the system’s resilience to disturbances and uncertainties, effectively mitigating SSO and improving overall system stability. Full article

Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m² solar farm under shaded conditions. Full article

This paper presents the design and performance evaluation of an experimental platform that emulates the static and dynamic behavior of a 3 kW Wind Energy Conversion System (WECS). The platform includes a wind turbine emulator (WTE) using a separately excited DC motor (SEDCM) as the prime mover, coupled with a grid-connected doubly-fed induction generator (DFIG). This setup enables comprehensive laboratory studies of a WECS without the need for large-scale field installations. A novel inertia compensation strategy is implemented to ensure the SEDCM accurately replicates the power and torque characteristics of a real wind turbine across various wind profiles. The DFIG was chosen for its high efficiency at variable wind speeds and its reduced power converter requirements compared to other generators. The control strategy for the DFIG is detailed, highlighting its performance and seamless integration within the system. Unlike most studies focusing on generators connected to simple loads, this research considers a grid-connected system, which introduces additional challenges and requirements. This study thoroughly investigates the grid-connected converter, addressing specific demands for grid connection and ensuring compliance with grid standards. Experimental results validate the effectiveness of the emulator, demonstrating its potential as a key tool for optimizing wind turbine control strategies and real-time algorithm validation, and enhancing the performance and reliability of renewable energy systems. Full article

Virtual synchronous generator (VSG) control, which can provide inertia output, damp power oscillations, and offer frequency and voltage support to power grids, has become a growing trend in the control field of wind power generation. As a new technology, there are still challenges that VSG control has not solved well, such as transient overvoltage suppression. A kind of transient overvoltage, which often occurs during the commutation failures of HVDC transmission systems, will trigger a mass of wind turbine generators (WTGs) disconnecting from grids. To reduce the grid-disconnection risk of the virtual synchronous generator control-based doubly fed induction generators (VSG-DFIGs), this paper first analyzes the mechanism of the automatic voltage regulation (AVR) control usually employed by VSG-DFIGs, then proposes measures to suppress the transient overvoltage. To solve the problem of the reactive power response lag issued by VSG-DFIGs, which will further aggravate the transient overvoltage in continuous low and high voltage faults, the time constant of the AVR control is switched. To fully exploit the potential of the DFIGs’ reactive power support, the droop coefficient of the AVR control is switched during the abnormal voltage stages. The switched droop coefficient will change the rotor excitation current magnitude, thus adjusting the internal potential of a DFIG, finally better supporting or suppressing the terminal voltage during the low or high voltage periods. Simulation results based on the DIgSILENT/PowerFactory platform demonstrate that the proposed method can effectively suppress the transient overvoltage that occurs in continuous low and high voltage events caused by the commutation failures of HVDC transmission systems, reducing the number of WTGs disconnecting from the grids. Full article

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink. Full article