MW Doubly Fed Induction Generator Research Articles

Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system

Energy ripples are among the common problems in renewable energies as a result of using less efficient strategies. In this work, a new technique is suggested to control a doubly-fed induction generator (DFIG) using the pulse width modulation (PWM). The new technique is based on the combination of neural networks and fractional-order control to minimize the reactive and active power ripples of the DFIG-based variable speed dual-rotor wind turbine system. The suggested fractional-order neural control (FONC) with the PWM is a simple, robust and a high-performance strategy. Simulation is performed using Matlab software to validate the effectiveness of the designed control of 1.5 MW DFIG and the obtained results are compared with the traditional direct power control (DPC) in different working conditions. In addition, the comparison between the suggested control and the DPC is performed in the cases of changing or not changing the device parameters in terms of ripple ratio, dynamic response, steady-state error, current quality, and overshoot of active and reactive power of the DFIG. As compared to the DPC, the proposed FONC technique improves the active and reactive power ripples by 65.71% and 84.74%, respectively. Also, improves the overshoot of the active and reactive power by 71.33% and 91.72%, respectively. The simulation results demonstrate the high performance and robustness of the FONC technique for the parametric variations of the DFIG-based variable speed dual-rotor wind turbine system compared to the DPC control.

Parameter Robustness Enhanced Deadbeat Control for DFIG with ESO-Based Disturbance Estimation

Doubly fed induction generators (DFIGs) are widely applied in wind energy conversion systems, where the harsh service environment and long-lasting operation can bring about motor parameter deviations, deteriorating the system performance. In this paper, an extended state observer (ESO)-based deadbeat control strategy that enhances the system parameter robustness is proposed. Firstly, the effects of motor parameter inaccuracy are analyzed to reflect the control errors and degradation of the system performance. Secondly, a lumped disturbance represented by an additional state extended from the system mathematical model is derived with the parameter inaccuracy taken into consideration. Finally, the parameter robustness enhanced deadbeat control method with the ESO-based disturbance estimation is developed to realize accurate prediction and control, even when the inductance of DFIG deviates under various operation conditions. To verify the effectiveness of the proposed method, simulations are carried out in MATLAB/Simulink for a 1.5 MW DFIG with a 30% stator and rotor inductance deviation. Compared to the conventional control method, smooth and fast dynamic performance is maintained, and the current ripple for the proposed control strategy can be reduced by approximately 40%, where the steady-state tracking performance and parameter robustness of the system are significantly enhanced.

Artificial intelligence-based controller for rotor current of doubly fed induction generator in wind turbine system

The demand for energy is increasing that can be met with Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS). In this paper, A 2 MW DFIG was used as the plant. To limit the shortcomings of a Proportional-Integral (PI) controller, Fuzzy Logic (FL), Fuzzy-PI, and Artificial Neuro-Fuzzy Inference System (ANFIS) controllers are being designed. The system is modeled in a MATLAB/Simulink. A comparative analysis of PI, Fuzzy, Fuzzy-PI, and ANFIS are presented. Taking a steady state error (SSE) as an objective function of performance index, the PI controller results with a 2.9084 A, Fuzzy with 0.8668 A, Fuzzy-PI with 7.654 A, and ANFIS, with 11.5472 A. Hence, the Fuzzy logic controller-based system is found to be the best candidate for SSE control of rotor current. An ANFIS-based controller has the best settling time for rotor currents control, whereas the Fuzzy-PI found to be best for SSE and torque control.

High-Gain Observer-Based Advanced Nonlinear Control of a Grid-Connected Wind Energy Conversion System with Sensorless Maximum Power Point Tracking

This paper deals with the control development of a wind energy conversion system (WECS) interfaced to a utility grid by using a doubly fed induction generator (DFIG), a back-to-back (B2B) converter and an RL filter for optimal power extraction. The aim was to design a sensorless controller to improve the system reliability and to simultaneously achieve the regulation of the generator speed, reactive power and DC-link voltage. The proposed global control scheme combines: (i) a high-gain observer employed to estimate the generator speed and the mechanical torque, usually regarded as accessible, (ii) a sensorless MPPT block developed to provide optimal generator speed reference, which is designed on the basis of the mechanical observer and a polynomial wind-speed estimator and (iii) a finite-time controller (FTC) applied to the B2B converter to meet the output reference’s tracking objectives in a short predefined finite time by using the backstepping and Lyapunov approaches. The proposed controller performance is formally analysed, and its capabilities are verified by numerical simulations using a 2 MW DFIG wind turbine (WT) under different operating conditions.

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

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

Nonlinear Control Strategies for Enhancing the Performance of DFIG-Based WECS under a Real Wind Profile

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

Optimal Pitch Angle Controller for DFIG-Based Wind Turbine System Using Computational Optimization Techniques

With the advent of high-speed and parallel computing, the applicability of computational optimization in engineering problems has increased, with greater validation than conventional methods. Pitch angle is an effective variable in extracting maximum wind power in a wind turbine system (WTS). The pitch angle controller contributes to improve the output power at different wind speeds. In this paper, the pitch angle controller with proportional (P) and proportional-integral (PI) controllers is used. The parameters of the controllers are tuned by computational optimization techniques for a doubly-fed induction generator (DFIG)-based WTS. The study is carried out on a 9 MW DFIG based WTS model in MATLAB/SIMULINK. Two computational optimization techniques: particle swarm optimization (PSO), a swarm intelligence algorithm, and a genetic algorithm (GA), an evolutionary algorithm, are applied. A multi-objective, multi-dimensional error function is defined and minimized by selecting an appropriate error criterion for each objective of the function which depicts the relative magnitude of each objective in the error function. The results of the output power flow and the dynamic response of the optimized P and PI controllers are compared with the conventional P and PI controller in three different cases. It is revealed that the PSO-based controllers performed better in comparison with both the conventional controllers and the GA-based controllers.

Intelligent controller for maximum power extraction of wind generation systems using ANN

Abstract The generation of clean energy from wind has recently received huge attention. Thanks to the current advances of adaptive algorithms due to their benefits and flexibility. The paper introduces a new smart radial basis function (RBF) neural network to extract the optimal energy from wind for wind energy conversion systems. This scheme uses the electrical energy of the doubly fed induction-generator (DFIG) as an input in wind turbines drives a DFIG to acquire maximum energy from the available wind under uncertainties and fast-changing wind conditions. Thus, to prove the quality of our proposed intelligent scheme, a comparative study with conventional optimum power is applied to a wind turbine driving a class of 1.5 MW DFIG during the transient operation. Furthermore, the analysis and the interpretation of raw and processed real measured data using the process of linear interpolation through Matlab/Simulink illustrate the relevance and the performance of the sensorless controller for the overall wind turbine system. Briefly, the numerical simulation studies show that a good efficiency and improved tracking of the smart RBF-neural network controller when implemented online below the real wind speed despite the unknown parameters.

Predictive direct torque control of a rotor-side matrix converter driven grid connected DFIG with torque and flux ripples minimisation criterion

The doubly fed induction generator (DFIG) is the most commonly used machine for variable speed wind power generation, and the single stage matrix converter (MC) can potentially replace the conventional back-to-back voltage source converters (VSCs) for rotor to grid inter connection. In this research work, the performance of grid connected DFIG with rotor side MC is analysed with predictive direct torque control (P-DTC) technique incorporating a criterion for minimising the torque and rotor flux ripples at a fixed switching frequency. Space vector modulation (SVM) is utilised for MC side control. The system exhibits better transient and steady-state behaviour over conventional configurations, for both sub and super-synchronous operating modes, as evaluated by simulation based on a 2 MW DFIG model in MATLAB-Simulink environment.

Improved dfig dftc by using a fractional-order super twisting algorithms in wind power application

Background: The direct flux and torque control are a robust, simple, and alternative approach control formulation that does not require decomposition into symmetrical components; the direct flux and torque control schemes have been proved to be preponderant for doubly-fed induction generators due to the simple implementation.
 Aim: This work presents the minimization of electromagnetic torque and rotor flux undulations of doubly-fed induction generators using fractional-order super twisting algorithms and modified space vector modulation techniques.
 Methods: The main role of direct flux and torque control is to regulate and control the electromagnetic torque and rotor flux of doubly-fed induction generators for wind turbine systems. The direct flux and torque control is a traditional control algorithm and robust technique. Fractional-order super twisting algorithms are a new and proposed nonlinear controller; characterized by a robust controller and a simpler algorithm, which gives a good harmonic distortion of current compared to other methods.
 Novelty: The A fractional-order super twisting algorithm is proposed. Proposed nonlinear controller construction is based on the traditional super twisting algorithm and fractional calculus to obtain a robust controller and reduces the electromagnetic torque and rotor flux undulations of doubly-fed induction generators. We use in our study a 1.5 MW doubly-fed induction generator integrated into a single-rotor wind turbine system to minimizes the electromagnetic torque, stator current, rotor flux undulations. As shown in the results figures using fractional-order super twisting algorithms ameliorate effectiveness especially minimizes the electromagnetic torque and rotor flux, and minimizes harmonic distortion of stator current (0.16 %) compared to the traditional control scheme.
 Results: As shown in the results figures using fractional-order super twisting algorithms ameliorate effectiveness especially minimizes the electromagnetic torque and rotor flux, and minimizes harmonic distortion of stator current (0.16 %) compared to the traditional control scheme.
 Conclusion: The direct flux and torque control are a robust, simple, and alternative approach control formulation that does not require decomposition into symmetrical components; the direct flux and torque control schemes have been proved to be preponderant for doubly-fed induction generators due to the simple implementation.

Modelling and control of vanadium redox flow battery for smoothing wind power fluctuation

Due to the negative impact of a highly stochastic wind power fluctuation on the power quality and stability during high penetration of wind power in power systems, there is growing interest in power smoothing and energy redistribution in wind power systems by using large-scale energy storage technologies. The aim of this work is to use a vanadium redox flow battery as an energy storage system (ESS) to smooth wind power fluctuation with two system configurations and corresponding control strategies. As the first step, a vanadium redox flow battery (VRFB) mathematical model, underlain by electrochemical theories, is built to describe the battery behaviours in charge/discharge cycles. Accordingly, the aforementioned system configuration and power fluctuation smoothing schemes are proposed. At the end of this work, simulations are conducted on a 3.6 MW doubly-fed induction generator (DFIG) at a diversity of wind speeds for an assessment of feasibility that this work can be applied to a high penetration of wind power in practice. Simulation results reveal that power fluctuations can be improved by power smoothing. The root mean square deviation (RMSD) percentage can be reduced to less than 1%.

Indirect active and reactive powers control of doubly fed induction generator fed by three-level adaptive-network-based fuzzy inference system – pulse width modulation converter with a robust method based on super twisting algorithms

Aim. This paper presents the minimization of reactive and active power ripples of doubly fed induction generators using super twisting algorithms and pulse width modulation based on neuro-fuzzy algorithms. Method. The main role of the indirect active and reactive power control is to regulate and control the reactive and active powers of doubly fed induction generators for variable speed dual-rotor wind power systems. The indirect field-oriented control is a classical control scheme and simple structure. Pulse width modulation based on an adaptive-network-based fuzzy inference system is a new modulation technique; characterized by a simple algorithm, which gives a good harmonic distortion compared to other techniques. Novelty. adaptive-network-based fuzzy inference system-pulse width modulation is proposed. Proposed modulation technique construction is based on traditional pulse width modulation and adaptive-network-based fuzzy inference system to obtain a robust modulation technique and reduces the harmonic distortion of stator current. We use in our study a 1.5 MW doubly-fed induction generator integrated into a dual-rotor wind power system to reduce the torque, current, active power, and reactive power ripples. Results. As shown in the results figures using adaptive-network-based fuzzy inference system-pulse width modulation technique ameliorate effectiveness especially reduces the reactive power, torque, stator current, active power ripples, and minimizes harmonic distortion of current (0.08 %) compared to classical control.

An Overview of Control Method with Various Crowbar Techniques of Wind Turbines during Power System Faults

This paper reviews the various control crowbar methods associated with doubly-fed induction generator (DFIG) wind energy system (WES) during power system faults. The crowbar control methods are designed to improve the fault ride-through (FRT) capability of DFIGs based on WESs. The adaptive neural-fuzzy inference system (ANFIS) is developed to detect the fault conditions and control the crowbar protection techniques. The proposed ANFIS crowbar protection technique detects the fault based on the measurement of the three phase voltages and currents at the terminals of the DFIG wind turbine (WT) to activate the crowbar protection system during fault period and deactivate it after fault clearance. Furthermore, the proposed ANFIS crowbar techniques are investigated to enhance the stability of studied WT generators and also, it designed to protect system components. All these simulations are carried for the model consists of 9 MW DFIG WTs connected to the grid model in Simulink/Matlab. Simulation results also revealed that outer terminal crowbar is more effective as compare with other crowbar techniques via ANFIS control strategy.

Modified Control Technique of DFIG for Power Quality Improvement

In this paper, a modified control technique for doubly fed induction generator (DFIG) is presented to improve its power quality by controlling (minimizing) grid current harmonics. The problems of harmonics injection in grid current, especially due to the presence of nonlinear loads, are very crucial. The proposed control technique can transfer the power from grid to DFIG and vice versa and can also control the DC link voltage (Vdc ) against wide variations in wind speeds. The maximum power point tracking (MPPT) is used in rotor side converter (RSC) to control the power flow through the DFIG. The MPPT extracts the maximum electromagnetic torque Tem from wind energy even during wide variations in wind speeds. A comparison of hysteresis current control (HCC) and space vector pulse width modulation (SVPWM) techniques for harmonic mitigation is presented considering all wind speeds. Also, the control technique for RSC and GSC is discussed in detail in this paper. Finally, the performance of the modified control technique to improve DFIG power quality is checked with the help of MATLAB/SIMULINK software considering 2.6 MW DFIG connected to a power grid.

Mutual Inductance Estimation Based Sensorless Adaptive Variance Controller for Doubly Fed Induction Generator

This article presents a novel robust adaptive speed control architecture that allows doubly fed induction generator (DFIG) to operate independently of any speed sensors or position encoders. The proposed speed sensorless control based on modified minimum variance adaptive control based speed observer makes the system robust to machine parameter variations and sensor malfunctions. The observer is formulated using a rotor current based reference adaptive system. To ensure robustness to current sensor failure, a modified adaptive rotor side converter control loop is proposed. The online identification for the adaptive control logic is based on the recursive least square estimation technique while the adaptive control law is based on an extended version of the minimum variance control algorithm. To ensure improved dynamic performance for the proposed control, a modification for the terminal voltage control loop is proposed. The reactive power reference for the vector control loop is calculated considering the rotor current component responsible for reactive power generation. The performance of the proposed control architecture is validated on a 1.5 MW DFIG modeled in MATLAB/Simulink and experimentally verified on hardware.

A new hybrid multilevel converter for DFIG-based wind turbines fault ride-through and transient stability enhancement

The increasing participation of wind turbines based on doubly fed induction generator (DFIG) in the power system around the world is motivating the investigation of novel strategies to enhance the reliability of DFIG and power system connection. This paper proposes the use of a new hybrid multilevel back-to-back converter on DFIG wind turbine to improve its fault ride-through capability and power system transient stability. The proposed topology is built adding single-phase full-bridge inverters in series with each phase of traditional three-phase inverter. This converter is able to generate higher voltages in rotor circuit whenever it is required, which can effectively protect power converter and at the same time contribute with grid transient performance. Simulation results, using PSCAD®, are presented for a 2 MW DFIG machine submitted to symmetrical faults in single machine—infinite bus and three-bus system. Results show that the proposal is effective as a protection and works well as an alternative to traditional solutions, limiting currents and maintaining power control during voltage dips. It is also effective in contribution with the improvement of transient stability margin by the possibility of reactive power injection.

Performance Investigation of Grid-Connected DFIG using Integrated Shunt Active Filtering Capabilities

This paper presents the modified grid side converter control (GSC) technique which enable the GSC to work as a shunt active filter to mitigate the grid current harmonics produced by the nonlinear load, as well as to transfer power from the grid to the rotor of doubly fed induction generator (DFIG) or vice versa. The main contribution of this proposed technique is an addition of a shunt active filter with space vector pulse width modulation (SVPWM) controller in GSC control itself in order to achieve a better grid current %THD profile, and simultaneously to control active power for variable wind speed. The reactive power supply to the DFIG and extraction of maximum power is achieved using RSC. The comparison of the modified GSC control technique using hysteresis current control (HCC), and SVPWM controller used to mitigate the harmonics is presented with different wind speeds. The proposed modified GSC control technique is simulated for grid-connected 2.6 MW DFIG based wind energy conversion system (WECS) in MATLAB Simulink environment.

Fault detection and classification in wind turbine by using artificial neural network

Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

Dynamic Behavior Analysis for Optimally Tuned On-Grid DFIG Systems

Metaheuristic Optimization Techniques (MOTs) such as the Artificial Bee Colony (ABC) algorithms and Grey Wolf Optimizer (GWO) can be conveniently used for reaching the Maximum Power Point Tracking (MPPT) of Wind Energy Conversion System (WECS). This paper presents an enhanced control strategy for both Rotor Side Converter (RSC) and Grid Side Converter (GSC) of the Doubly Fed Induction Generator (DFIG)-based WECS using the ABC and the GWO algorithms to ensure the MPPT for the WECS. The control strategy for the RSC and GSC are verified via 9 MW DFIG Wind Turbine (WT) using MATLABTM/Simulink. The dynamic performance improvement of the DFIG depends on the appropriate choice of the optimal PI controllers’ gains. The numerical simulation results show the superiority of the proposed GWO-PI and the ABC-PI optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance.

Dynamic Modeling and Robust Controllers Design for Doubly Fed Induction Generator-Based Wind Turbines under Unbalanced Grid Fault Conditions

High penetration of large capacity wind turbines into power grid has led to serious concern about its influence on the dynamic behaviors of the power system. Unbalanced grid voltage causing DC-voltage fluctuations and DC-link capacitor large harmonic current which results in degrading reliability and lifespan of capacitor used in voltage source converter. Furthermore, due to magnetic saturation in the generator and non-linear loads distorted active and reactive power delivered to the grid, violating grid code. This paper provides a detailed investigation of dynamic behavior and transient characteristics of Doubly Fed Induction Generator (DFIG) during grid faults and voltage sags. It also presents novel grid side controllers, Adaptive Proportional Integral Controller (API) and Proportional Resonant with Resonant Harmonic Compensator (PR+RHC) which eliminate the negative impact of unbalanced grid voltage on the DC-capacitor as well as achieving harmonic filtering by compensating harmonics which improve power quality. Proposed algorithm focuses on mitigation of harmonic currents and voltage fluctuation in DC-capacitor making capacitor more reliable under transient grid conditions as well as distorted active and reactive power delivered to the electric grid. MATLAB/Simulink simulation of 2 MW DFIG model with 1150 V DC-linked voltage has been considered for validating the effectiveness of proposed control algorithms. The proposed controllers performance authenticates robust, ripples free, and fault-tolerant capability. In addition, performance indices and Total Harmonic Distortions (THD) are also calculated to verify the robustness of the designed controller.