This work deals with the study and performance improvement of the direct power control of DFIG based on backstepping Controller. Direct power control using hysteresis regulator has certain disadvantages such as significant powers ripples, variable switching frequency and sensitivity to parametric variations. To surmount these disadvantages, we present a robust controller such as the backstepping-based direct power control using SVM. A comparison study was made between the classic direct power control and the backstepping controller. The simulation results show that the backstepping controller provides good results reduces powers ripples.

The direct control power (DPC) of the of the double feed induction generator (DFIG) using conventional controllers based on PI regulators is characterized by poor results: Robustness properties are not guaranteed in the face of parametric uncertainties and strong ripple of the powers. From the best evoked control techniques presented in this field to overcome these drawbacks, we will study some improvement variants such as the use of The second order sliding mode control (SOSMC) developed on the basis of the super twisting torsion algorithm (STA) associated with the fuzzy logic control to obtain (FSOSMC) in order to obtain acceptable performance. Finally, the effectiveness of the planned control system is studied using Matlab/Simulink. The proposed method that not only reduces power ripples, but also improves driving dynamics by making it less sensitive to parameter uncertainty.

In this paper a dual direct torque control (DDTC) strategy with second-order sliding mode control (SOSMC) controller of the doubly fed Induction motor (DFIM) is presented in order to overcome some drawback such as ripples in torque, flux and to improve dual direct torque control (DDTC) performance toward the electrical parameters variations. This control strategy used in the doubly fed induction machine supplied, coupled by two voltage source inverters in rotor and stator sides witches are linked to two switching tables in order to determined the rotor and stator flux vector control. This controller based on super-twisting algorithm (STA). Comparative results between a classical controller (PI) and the proposed controller can prove the very satisfactory performance and robustness of this new controller.

 

This paper proposes the fundamental aspects of hybrid nonlinear control which is composed of the super twisting algorithm (STA) based second order sliding mode control applying fuzzy logic method (FSOSMC), with pertinent simulation results for a doubly fed induction machine (DFIM) drive. To minimize chattering effect phenomenon due to Signum function employed in sliding mode algorithm, a new method is proposed. This technique consists in replacing the signum function by fuzzy switching function in the SOSMC to minimize flux and torque ripples. This FSOSMC is associated to the double direct torque control DDTC of the doubly fed induction machine (DFIM) by combining the advantages of fuzzy logic (FL) and the advantages of super-twisting sliding mode. The FSOSMC-DDTC strategy is compared with a PI-DDTC and SOSMC-DDTC. Simulation results demonstrate good efficiency and excellent robustness of the hybrid nonlinear controller.

This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

This papers deal with a new Adaptive Direct Power Control for Doubly-Fed Induction Generator of 1.5 MW. The main feature of the proposed strategy is based on the replacement of the fixed switching table by an adaptive one. The online update of the adaptive switching table depends on the reactive power variation and past switching sequences. The proposed adaptive direct power control is compared with Vector Control and Classical Direct Power Control. The robustness of the proposed control scheme against parameter, load and wind speed variations have done with success. The main performance of the Adaptive Direct Power Control strategy is the reduction of powers ripples, thus reduce of torque ripple on the shaft of the turbine.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

This paper presents a fuzzy logic controller destined to the doubly-fed induction motor (DFIM) speed controlling. It solves the problems associated with the conventional IP (Integral Proportional) controller. This fuzzy logic controller is based on the decoupling control to enhance robustness under different operating conditions such as load torque and in the presence of parameters variation. The simulation results for various scenarios show the high performances of the proposed control in terms of piloting effectiveness, precision, rapidity and stability for the high powers DFIM operating at variable speeds.

In order to illustrate the criteria relating to static and dynamic performance, complexity of design, implementation and decoupling, a comparison was made between two asynchronous machine control techniques which are the direct torque control under the base of the SVM (DTC-SVM) and the predictive torque control (MPDTC). The results show the merits of these two control strategies with respect to the variations of the load setpoint for the torque and its influence on the speed and were obtained using a simulation program under the environment Matlab / Simulink