Showing 12 results for Dfig
M. Hosseinabadi, H. Rastegar,
Volume 10, Issue 4 (12-2014)
Abstract
This paper is concerned with behavior analysis and improvement of wind turbines with Doubly Fed Induction Generator (DFIG) when using a new fractional-order control strategy during wind variations. A doubly fed induction generator, two types of variable frequency power electronic converters and two input wind waveforms are considered. A fractional-order control strategy is proposed for the wind turbine control unit. Output parameters of the wind turbine are drawn by simulations using MATLAB/Simulink for both fractional-order and integer-order (classic) control systems and a complete comparison between these two strategies has been presented. Results show a better operation when using fractional-order control system.
H. Ahmadi, A. Rajaei, M. Nayeripour, M. Ghani,
Volume 14, Issue 4 (12-2018)
Abstract
Considering the increasing usage of the clean and renewable energies, wind energy has been saliently improved throughout the world as one of the most desired energies. Besides, most power houses and wind turbines work based on the doubly-fed induction generator (DFIG). Based on the structure and the how-ness of DFIG connection to the grid, two cases may decrease the performance of the DFIG. These two cases are known as a fault and a low-voltage in the grid. In the present paper, a hybrid method is proposed based on the multi-objective algorithm of krill and the fuzzy controller to improve the low-voltage ride through (LVRT) and the fault ride through (FRT). In this method, first by using the optimal quantities algorithm, the PI controllers’ coefficients and two variables which are equal to the demagnetize current have been calculated for different conditions of fault and low voltage. Then, these coefficients were given to the fuzzy controller. This controller diagnosed the grid condition based on the stator voltage and then it applied the proper coefficients to the control system regarding the diagnosed condition. To test the proposed method, a DFIG is implemented by taking the best advantages of the proposed method; additionally, the system performance has been tested in fault and low voltage conditions.
E. Heydari, M. Rafiee, M. Pichan,
Volume 14, Issue 4 (12-2018)
Abstract
Among a multitude of diverse control methods proposed for doubly fed induction generator (DFIG) based-wind energy conversion systems, direct power control (DPC) method has demonstrated superior dynamic performance and robustness in presence of disturbances. However, DPC is not a flawless method and shortcomings like necessity for high sampling frequency, high-speed sensors and less noise-affected sampling circuit need to be mitigated by utilizing fuzzy controllers. Parameter setting in a fuzzy controller plays a vital role, especially under non-ideal grid conditions. In this paper, a fuzzy-genetic algorithm-based direct power control (FGA-DPC) method is proposed for DFIG, while, the parameters of the fuzzy controller are optimized by genetic algorithm. The objective of the optimization is to minimize the stator active and reactive power errors to increase the precision of reference tracking. The objectives of the controller are also optimizing active power absorption based on the zone of operation and adjustment of reactive power according to grid requirements. The proposed method improves the overall precision and speed of transient response as well as significantly reducing power oscillations under non-ideal grid conditions. Finally, to demonstrate the effectiveness of the proposed method, extensive simulations are performed in Matlab/Simulink under different conditions.
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 15, Issue 1 (3-2019)
Abstract
This article presents an improved direct vector command (DVC) based on intelligent space vector modulation (SVM) for a doubly fed induction generator (DFIG) integrated in a wind turbine system (WTS). The major disadvantages that is usually associated with DVC scheme is the power ripples and harmonic current. To overcome this disadvantages an advanced SVM technique based on fuzzy regulator (FSVM) is proposed. The proposed regulator is shown to be able to reduce the active and reactive powers ripples and to improve the performances of the DVC method. Simulation results are shown by using Matlab/Simulink.
Z. Rafiee, M. Rafiee, M. R. Aghamohammadi,
Volume 16, Issue 3 (9-2020)
Abstract
Improving transient voltage stability is one of the most important issues that must be provided by doubly fed induction generator (DFIG)-based wind farms (WFs) according to the grid code requirement. This paper proposes adjusted DC-link chopper based passive voltage compensator and modified transient voltage controller (MTVC) based active voltage compensator for improving transient voltage stability. MTVC is a controller-based approach, in which by following a voltage dip (VD) condition, the voltage stability for the WF can be improved. In this approach, a voltage dip index (VDI) is proposed to activate/deactivate the control strategy, in which, two threshold values are used. In the active mode, the active and reactive power are changed to decrease the rotor current and boost the PCC voltage, respectively. Based on the control strategy, in a faulty grid, DFIG not only will be able to smooth DC-link voltage fluctuations and reduces rotor overcurrents but also it will increase the voltage of point of common coupling (PCC). Therefore, it improves transient voltage stability. The simulation results show the effectiveness of the proposed strategy for improving voltage stability in the DFIG.
Y. Djeriri,
Volume 16, Issue 4 (12-2020)
Abstract
In this work, a robust nonlinear control technique of a doubly fed induction generator (DFIG) intended for wind energy systems has been proposed. The principal idea in this article is to decouple the active and reactive power of the DFIG with high robustness using the backstepping strategy. The principle of this control method is based on the Lyapunov function, in order to guarantee the global asymptotic stability of the system. Finally, we present some simulation results in order to verify the efficiency and robustness of the proposed control technique.
H. Benbouhenni, Z. Boudjema, A. Belaidi,
Volume 17, Issue 1 (3-2021)
Abstract
The paper presents a super-twisting sliding mode (STSM) regulator with neural networks (NN) of direct power command (DPC) for controlling the active/reactive power of a doubly-fed induction generator (DFIG) using a two-level space vector pulse width modulation (2L-SVPWM). Traditional DPC strategy with proportional-integral (PI) controllers (DPC-PI) has significantly more active/reactive power ripples, electromagnetic torque ripple, and harmonic distortion (THD) of voltages. The proposed DPC strategy based on a neural super-twisting sliding mode controller (NSTSM) minimizes the THD of stator/rotor voltage, reactive/active power ripple, rotor/stator current, and torque ripples. Also, the DPC method with NSTSM controllers (DPC-NSTSM) is a simple algorithm compared to the vector control method. Both methods are developed and programmed in Matlab on a 1.5MW DFIG-based wind turbines. The simulation studies of the DPC technique with the NSTM algorithm have been performed, and the results of these studies are presented and discussed.
R. Rezavandi, D. A. Khaburi, M. Siami, M. Khosravi, S. Heshmatian,
Volume 17, Issue 2 (6-2021)
Abstract
Recently, Brushless Cascaded Doubly Fed Induction Generator (BCDFIG) has been considered as an attractive choice for grid-connected applications due to its high controllability and reliability. In this paper, a Finite Control Set Model Predictive Control (FCS-MPC) method with active and reactive power control capability in grid-connected mode is proposed for controlling the BCDFIG in a way that notable improvement of the dynamic response, ripple reduction of the active and reactive power waveforms and also better THD performance are achieved compared to the traditional approaches such as Vector Control (VC) method. For this purpose, the required mathematical equations are obtained and presented in detail. In order to validate the proposed method performance, a 1–MW grid-connected BCDFIG is simulated in MATLAB/Simulink environment.
H. Zahedi Abdolhadi, Gh. Arab Markadeh, S. Taghipour Boroujeni,
Volume 17, Issue 3 (9-2021)
Abstract
Classical structure of Doubly Fed Induction Generators (DFIGs) is not completely adapted in high-speed regions due to their brushes and slip rings. So in the Cascaded DFIGs (CDFIGs), the rotor windings of a given DFIG are supplied by another wound rotor induction machine leading to a complete brushless structure. This paper presents and compares Sliding Mode Control (SMC) and Terminal Sliding Mode Control (TSMC) methods to control the output voltage of CDFIG. The SMC and TSMC methods are identified as strong controllers with large stability and robustness margins. In this paper, the SMC and TSMC methods are evaluated and compared to the conventional Voltage Oriented Control (VOC) in terms of output voltage change, prime over speed’s variation, and nonlinear load. Simulation and experimental results using a TMS320F28335 based prototype system show that the SMC and TSMC techniques are more robust against parameter variations and uncertainties, and TSMC offers improved dynamic response.
M. Kamarzarrin, M. H. Refan, P. Amiri, A. Dameshghi,
Volume 18, Issue 2 (6-2022)
Abstract
One of the major faults in Doubly-Fed Induction Generator (DFIG) is the Inter-Turn Short Circuit (ITSC) fault. This fault leads to an asymmetry between phases and causes problems to the normal state between current lines. Faults diagnosis from non-stationary signals for the Wind Turbine (WT) is difficult. Therefore, the strategy of fault diagnosis must be robust against instability. In this paper, a new intelligent strategy based on multi-level fusion is proposed for diagnosis of DFIG inter-turn stator winding fault. Firstly, to overcome the non-stationary nature of the vibration signals of the WT, empirical mode decomposition (EMD) method is performed in time-frequency domains to extract best fault features from information power sensor and information current sensor. Moreover, a feature evaluation technique is used for the input of the classifier to choose the best subset features. Secondly, Least Squares Wavelet Support Vector Machines (LS-WSVM) classifier is trained to classify fault types based on feature level fusion (FLF) from different sensors. The main parameters of SVM and the kernel function are optimized by Genetic Algorithm (GA). Finally, Dempster-Shafer evidential reasoning (DSER) is used for fusing the GA-LS-WSVM results based on decision level fusion (DLF) of individual classifiers. In order to evaluate the proposed strategy, a DFIG WT test rig is developed. The experimental results show the efficiency of the proposed structure compared to other ITSC fault diagnosis methods. The results show that the classification accuracy of DSER-GA-LS-WSVM is 98.27%.
Y. Fattahyan, N. Ramezani, I. Ahmadi,
Volume 18, Issue 3 (9-2022)
Abstract
Using doubly-fed induction generator (DFIG) based onshore wind farms in power systems may lead to mal-operation of the second zone (Z2) of distance protection due to the uncertain number of available wind turbines on the one hand and the function of DFIGs control system to maintain the bus voltage on the other hand. In such cases, variable injected current by the wind farm causes distance relay fall in trouble to distinguish whether the fault point is in the Z2 operating area or not. In the current study, an adaptive settings scheme is proposed to determine the Z2 setting value of distance relays for such cases. The proposed method is based on the adaptive approach and the settings group facility of the commercial relays. The proposed method applies the k-means clustering approach to decrease the number of setting values calculated by the adaptive approach to the number of applicable settings group in the distance relay and uses the Particle Swarm Optimization (PSO) algorithms to achieve the optimum setting values. The high accuracy of the proposed method in comparison with other methods, suggested in the literatures, is shown by applying them to the IEEE 14-bus grid.
Azzedine Khati,
Volume 20, Issue 3 (9-2024)
Abstract
In this research paper, a multivariable prediction control method based on direct vector control is applied to command the active power and reactive power of a doubly-fed induction generator used into a wind turbine system. To obtain high energy performance, the space vector modulation inverter based on fuzzy logic technique (fuzzy space vector modulation) is used to reduce stator currents harmonics and active power and reactive power ripples. Also the direct vector control model of the doubly-fed induction generator is required to ensure a decoupled control. Then its classic proportional integral regulators are replaced by the multivariable prediction controller in order to adjust the active and reactive power. So, in this work, we implement a new method of control for the doubly-fed induction generator energy. This method is carried out for the first time by combining the MPC strategy with artificial intelligence represented by Fuzzy SVM-based converter in order to overcome the drawbacks of other controllers used in renewable energies. The given simulation results using Matlab software show a good performance of the used strategy, particularly with regard to the quality of the energy supplied.
