DFIG-based Wind Turbine Research Articles

Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance

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.

Multi-Objective Optimization and Intelligent Algorithm Study for 1.5mw Doubly Fed Wind Turbine Blades

Background: The demand for renewable energy has catapulted wind power to the forefront of sustainable energy options. Designing wind turbine blades is a multi-objective optimization problem with conflicting objectives. Optimization methods such as the weighted sum or the goal programming method fail in this context of conflicting objectives: minimum weight, cost, and strength. The study discusses the integration of artificial intelligence algorithms with conventional design approaches for optimal blades of a 1.5MW DFIG wind turbine. Advanced computational methods are used to integrate aerodynamic efficiency, structural durability, and economic feasibility. Methods: This paper uses a broad meta-analytical approach with the integration of finite element analysis (FEA)and artificial intelligence optimization algorithms. This paper will consider three critical optimization techniques: genetic algorithms (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO). The blade performance will be considered for various operating conditions, including aerodynamic efficiency, structural integrity, and cost. This analysis will be performed based on IEC 61400 standards and new frontiers of innovative design optimization techniques. Results: Blade design optimization showed considerable improvement in using AI-driven approaches. For the GWO algorithm, better convergence is around 20% faster than the one obtained with traditional methods. This optimized design reduces weight by 8% and improves structural durability by an increase of 25% in fatigue life. For combined blade design, the maximum value of the power coefficient is 0.27, thus showing considerable improvement compared to the conventional designs. Besides, innovative material selection and design optimization could reduce the cost index by 17.6%. Conclusion: These results highlight that incorporating AI algorithms with traditional methods has significantly enhanced wind turbine blade optimization. This methodology was developed in such a way that it achieved a balanced solution for multi-objective designs, including computational efficiency. This study stresses industrial feasibility by using advanced optimization techniques for real applications, thus demonstrating their impact on the future of wind energy technological development.

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

Optimized Design of Demagnetization Control for DFIG-Based Wind Turbines to Enhance Transient Stability During Weak Grid Faults

Optimized Design of Demagnetization Control for DFIG-Based Wind Turbines to Enhance Transient Stability During Weak Grid Faults

Improved LVRT Strategy for DFIG-based Wind Turbine Considering RSC-GSC Interaction during Symmetrical Grid Faults

Improved LVRT Strategy for DFIG-based Wind Turbine Considering RSC-GSC Interaction during Symmetrical Grid Faults

Analysis and model of high frequency harmonic impedance of grid connected DFIG wind turbines

Analysis and model of high frequency harmonic impedance of grid connected DFIG wind turbines

Optimal control strategies for DFIG-based wind turbines: simulation and analysis

Doubly fed induction generators (DFIGs) have become a cornerstone technology in modern wind turbine systems, thanks to their ability to operate across a wide range of rotational speeds. This flexibility enables DFIG-based wind turbines to achieve the optimal power coefficient (Cpopt), thereby maximising energy capture from variable wind conditions. This study presents a comprehensive mathematical model of the induction machine in the dq domain, followed by the development of a vector control model tailored to the unique characteristics of DFIGs. A simplified model for the wind turbine, including its torque controller, is also formulated to integrate seamlessly with the DFIG model. To validate the proposed control strategies, a series of simulations were conducted, demonstrating their effectiveness in optimising the performance of wind energy systems under varying operating conditions.

Enhancing vector control performance in DFIG-based wind turbine through ANFIS controller using real variable wind speed profile

This article focuses on enhancing vector control for a Doubly Fed Induction Generator (DFIG) through the integration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method and comparing it with the classical Proportional-Integral (PI) controller. In this study, the wind turbine operates on a DFIG connected to the AC power grid. The proposed vector-ANFIS controller is implemented on the rotor-side converter to regulate the current and achieve the desired torque for Maximum Power Point Tracking (MPPT). The system was modeled and simulated using MATLAB Simulink. The simulation employed a real variable wind speed profile to reflect a real-world scenario. our contribution was also to compress several hours of collected data into a matter of seconds, enabling us to simulate a multi-hour period and observe the results in MATLAB. The results demonstrate the superior performance of ANFIS compared to PI, indicating its potential utility in enhancing the power quality and stability of wind power systems.

Observer based finite-time adaptive fractional-order sliding mode control of DFIG wind turbines with unknown dynamic parameters

In this study, we investigate adaptive finite-time fractional-order sliding mode control (AFFSMC) of DFIG wind turbines (DWTs) under model uncertainty in finite time. A fractional-order sliding surface is first constructed to develop the concept. A robust terminal adaptive sliding mode controller is then designed for pitch angle control of wind turbine to achieve finite-time stability at high wind speeds to regulate the produced electric power in Zone III of the turbine’s working range. This is all with this assumption that there is no any information about the dynamics of the turbine due to the significant uncertainties. Through the use of a stable adaptive law, the vector of the wind turbine’s unknown dynamic parameters is approximated in this method. An observer is also proposed to estimate both turbine speed and external disturbances term. The closed-loop system’s finite-time stability study and the finite-time convergence of the turbine output were both carried out using the dynamic model of the system and the proposed Lyapunov theory. Some numerical examples are also presented together with a comparison to the standard integer sliding mode control to demonstrate the correctness of the control law.

A Simplified Dynamic Model of DFIG-based Wind Generation for Frequency Support Control Studies

In recent years, wind generation has been fast growing and become one of the major generation resources in power systems. Since the wind generation does not inherently equip with frequency support functions, the power system frequency response degrades as the wind penetration increases. Therefore, frequency support control of wind generation has gained increasing focus. However, the dynamic models of wind generation systems, which are required by the control design and analysis, are commonly very complicated and difficult to obtain, especially for the doubly-fed induction generator based wind turbine (DFIG-WT). In this paper, the authors propose a simplified dynamic model for the DFIG-WT. The proposed analytical model is derived from the detailed model by selectively neglecting very fast dynamics while keeping the critical ones. It is suitable for frequency control studies. The model accuracy is first validated against the detailed DFIG-WT model in Simulink. Then, the wind frequency control function is studied based on the proposed model.

Tracking performance and power quality enhancement of DFIG based wind turbine using robust integral terminal sliding mode control

The aim of this work is to develop a robust Integral Terminal Sliding Mode Control (ITSMC) strategy for a Doubly Fed Induction Generator (DFIG) wind turbine (WT) operating in challenging conditions. This controller is designed to manage the system's rotor side converter (RSC) and grid side converter (GSC), ensuring maximum power production, improved power quality, and accurate tracking of system state variables, even in the face of fluctuating wind conditions, parameter uncertainties, and external disturbances. The stability of the proposed controller is assured using the Lyapunov theory. The performance of ITSMC is compared with that of the conventional sliding mode controller (SMC) and backstepping controller through simulation tests, using the integral absolute error (IAE) as a performance metric. The results highlight the superiority of ITSMC, demonstrating its ability to precisely track reference values, eliminate static errors, and minimize chattering. ITSMC consistently achieves the lowest IAE for all tracked state variables and across all test scenarios. A comparative study analyzing the Total Harmonic Distortion (THD) of the proposed controller versus SMC, the backstepping controller, and other similar works in the literature shows a marked improvement in the THD of stator and filter currents by at least 24.53 % and 7.96 %, respectively. These findings underscore the controller's capability to enhance the power quality delivered to the grid.

Fuzzy logic based improved direct torque control of DFIG-based wind turbines

This paper investigates direct torque control with a fuzzy controller (F-DTC) for a two-level voltage source inverter feeding the rotor of a doubly-fed induction generator (DFIG) based on a variable-speed wind energy conversion system (VSWECS). The wind turbine's MPPT technology is used to obtain the torque reference. The proposed fuzzy logic-based direct torque control (F-DTC) is designed to eliminate the flux and electromagnetic torque ripples that are common with existing (DTC) controls, leading to smoother operation and improved efficiency in the wind energy conversion system. The fuzzy logic controller improves switching decision-making by handling uncertainties and non-linearities, resulting in smoother and more efficient control of the torque and flux. To validate the effectiveness of the proposed F-DTC method, simulations are conducted in MATLAB/SIMULINK, and the results are compared with those of the classical DTC approach. The findings demonstrate that the F-DTC strategy significantly reduces torque and flux ripples, enhances the dynamic response, and improves overall system performance.

Using the proportional dual integral strategy to improve the characteristics of the indirect field-oriented control of DFIG-based wind turbine systems

In this work, a new indirect field-oriented command (IFOC) for wind systems based on a doubly-fed induction generator (DFIG) is designed to get better energy and stream quality. Also, overcoming the problems of IFOC. It is proposed to use proportional dual integral (PDI) controllers in order to replace traditional controllers and augment the robustness of the studied wind turbine system. The proposed IFOC-PDI is a modification of the IFOC, where the basic idea behind the designed technique is to combine a proportional-integral (PI) regulator with an integral term. This technique has been applied to the machine inverter, where the IFOC-PDI aims to calculate reference voltage values. The pulse width modulation (PWM) is used to translate voltage reference values ​​into pulses to run the machine inverter. The main objective of this article is to compare the competence of the IFOC-PDI to that of the IFOC-PI in different tests, including two different wind speed profiles and DFIG parameters variation. The major benefit of this command is a decrease in harmonic distortion of the currents, and active and reactive power (Ps and Qs) fluctuations. The results using MATLAB demonstrated that the IFOC-PDI improved the IFOC-PI in the minimization of stream harmonic distortion (6.63%, 10.42%, and 17.86%) and notable minimization of fluctuations in both Ps (32.08%, 46.24%, and 12.10%) and Qs (45.95%, 66.88%, and 14.49%). Also, the overshoot value of Ps was minimized compared to the IFOC-PI in all tests by ratios estimated at 57.79%, 12.41%, and 33.96%. The steady-state error of Ps was minimized compared to the IFOC-PI in all tests by ratios estimated at 29.43%, 85.88%, and 76.36%. The IFOC-PDI is also very resistant in the case of DFIG parameters variation.

Adaptive passive fault tolerant control of DFIG-based wind turbine using a self-tuning fractional integral sliding mode control

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field- oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances.

Disturbance observer-based finite-time adaptive neural control scheme of DFIG-wind turbine

This paper introduces a novel disturbance observer-based finite-time adaptive neural control approach to optimize wind power conversion in a doubly fed induction generator-based wind turbines (DFIG-WT). This control strategy offers appealing features including rapid finite-time convergence, both transient and steady-state performance enhancements, and robustness against external disturbances and inherent model uncertainties. The control strategy integrates the neural networks estimation capability with the interesting proprieties of the finite-time control method to achieve efficient wind power conversion. Closed-loop finite-time stability is conducted using the finite-time Lyapunov stability concept of nonlinear systems. The developed control strategy’s effectiveness is confirmed through numerical simulation.

FRT Capability Enhancement of DFIG-based Wind Turbine with Coordination Control of MSVC and FCL

Objective: To address the problems of various FRT (fault ride through) schemes in DFIG (doubly fed induction generator) systems, especially their applicability under different voltage sags, a combination scheme of an MSVC (minimized series voltage compensator) and FCL (fault current limiter) is proposed. Methods: Based on the analysis of the mathematical model of the DFIG and considering the capacity and volume in the process of practical engineering, the application structure and specific control strategy of an MSVC in DFIG systems are designed on the stator side, and the application effect is analyzed theoretically. Simultaneously, the application structure and control strategy of the FCL is proposed on the rotor side, and the application effect of the combination scheme is theoretically deduced and analyzed. Moreover, the simulation model is built on the MATLAB/Simulink platform. Results: The simulation results show that the scheme can quickly and effectively recover the fault voltage of the DFIG under different voltage sag degrees and has better dynamic performance. At the same time, it can effectively limit the fault current and suppress the DC-link voltage of the rotor side, and the transition process is relatively stable. Conclusion: The purpose of improving the FRT capability of the DFIG system is realized.

IoT based monitoring system for DFIG based wind turbines under voltage dips

Due to their partial-rating power converters and dynamic performance, doubly-fed induction generators (DFIGs) are commonly used in wind turbines (WT). However, grid-connected DFIGs are vulnerable to voltage dips, which can cause power quality concerns and possible turbine damage. An Internet of Things (IoT)-based monitoring technique for field-oriented control (FOC)-based DFIG with grid-side converter (GSC) control is proposed in this paper. The proposed approach uses real-time WT voltage and current data to dynamically alter the GSC control settings, allowing for steady operation and minimizing power quality problems during voltage dips. A 2 MW variable-speed pitch-controlled DFIG with a speed limit of 900–2000 rpm is used for performance investigation under voltage dips with crowbar protection. An IoT based monitoring system sends real-time information to a cloud server for additional analysis after detecting anomalies. The ThingSpeak cloud platform sends email notifications of defects, and an MIT App Inventor application is constructed for data visualization. The proposed strategy's effectiveness is confirmed through the simulation results.

Novel chattering free binomial hyperbolic sliding mode controller for asymmetric cascaded E-type bonded T-type multilevel inverter-based dynamic voltage restorer to meliorate FRT capability of DFIG-based wind turbine

The endurance capability of Doubly Fed Induction Generator (DFIG)-based wind turbine to network against different disturbances is an important criterion to meet the recent grid codes requirements. This generator has to provide the required energy under the perturbation conditions which is principally specified by Fault Ride-Through (FRT) capability. It refers to safe and continuous operation of DFIG without interruption which has been performed by Dynamic Voltage Restorer (DVR) using fast and accurate compensation of the voltage fluctuations. This paper aims to introduce an innovative quasi-sinusoidal voltage synthesizer for DVR to enhance the power quality and power delivery. Hence, a new Asymmetric Cascaded E-type Bonded T-type Multilevel Inverter (ACEBTMI) with binary geometric progression form of DC sources is proposed to provide high-step staircase sinusoidal voltage containing low semiconductor switches. Then, accurate triggering signals for the suggested ACEBTMI corresponding to the voltage fluctuations have been provided by new Chattering Free Binomial Hyperbolic Sliding Mode Controller (CFBHSMC). To validate compensation capability of the proposed DVR, ACEBTMI part has been compared with some traditional and familiar multilevel inverters, and also CFBHSMC part has been compared with SMC, FLC and PID controllers. In precise, the simulation results have definitely confirmed the accurate tracking and robust control performance of CFBHSMC to support ACEBTMI-based DVR which enables DFIG to ride-through different voltage perturbations.

Advanced Control Solutions for DFIG Wind Turbines: From Vector to Predictive Control

This paper investigates advanced control strategies for a doubly-fed induction generator-based wind turbine (DFIG-WT), focusing on vector control and model predictive control (MPC). Initially, it introduces the DFIG-WT structure and general control methods, then explores vector control for effective decoupled power management of active and reactive power. Despite vector control's efficiency, its limitations in handling constraints are highlighted, leading to the adoption of MPC. The study examines MPC's application in current control for three-phase two-level converters, showing its capability for fast tracking and response. Further, it explores MPC's model predictive torque control, demonstrating its proficiency in handling various requirements through a well-designed cost function. Overall, this research emphasizes MPC's adaptability and efficiency in optimizing DFIG-WT performance under varying operational conditions.

Estimating the region of attraction of wind integrated power systems based on improved expanding interior algorithm

AbstractThis paper proposes an improved expanding interior algorithm (EIA) to estimate the region of attraction (ROA) of power systems with wind power generation based on sum of squares (SOS) programming. An ordinary differential equation (ODE) model is derived for the doubly‐fed induction generator‐based wind turbine (DFIGWT), which is named as an enhanced synchronous‐generator‐mimicking (ESGM) model. The ESGM model bridges the gap between the requirement of an ODE model in ROA estimation and the conventional differential‐algebraic equation (DAE) model of the DFIGWT system. The ESGM model is able to accurately reflect the low frequency dynamics of the DFIGWT. Moreover, an improved EIA is designed to estimate the ROA based on SOS programming, which has higher efficiency than the existing ROA estimation algorithms based on SOS programming. It is able to adaptively search for the Lyapunov function and obtain an optimal estimation of the ROA in an iterative process. The accuracy and efficiency of this algorithm are verified in three test systems composed of DFIGWTs and synchronous generators (SGs). The morphological changes in the ROA of the test systems caused by the penetration of DFIGWT are examined.