Doubly fed induction

In this article, we have set up a vector control law of induction machine where we tried different type of speed controllers. Our control strategy is of type Field Orientated Control (FOC). In this structure we designed a Fuzzy Gain-Scheduling Proportional-Integral (Pi) controller to obtain best result regarding the speed of induction machine. At the beginning we designed a Pi controller with fixed parameters. We came up to these parameters by identifying the transfer function of this controller to that of Broïda (second order transfer function). Then we designed a fuzzy logic (FL) controller. Based on simulation results, we highlight the performances of each controller. To improve the speed behaviour of the induction machine, we have designend a controller called "Fuzzy Gain-Scheduling Proportional-Integral controller" (FGS-PI controller) which inherited the pros of the aforementioned controllers. The simulation result of this controller will strengthen its performances.

The objective of this work is to study the contribution that the use of the multi-level inverter can make compared to a conventional two-level inverter, in a wind power production line (WPG) associated with a doubly fed induction generator (DFIG). The DFIG is driven by a variable speed wind turbine and operates in maximum power point tracking (MPPT) mode, for optimum efficiency. The rotor of the DFIG is supplied by a DC/AC inverter with five levels with MPC structure, controlled by the PWM technique, while the stator is connected with the network. The active and reactive powers exchanged between the DFIG and the network is achieved by indirect vector control with oriented stator flux (IFOC), with conventional regulators, ensuring zero reactive power and a unitary power factor. The total harmonic distortion (THD) of the current signals/voltages of the entire wind chain is exposed and criticized. The obtained results are very promising, offering the possibility for wind turbines with multi-level inverter to work in high voltage and large power.

In this paper, using synthesis of a hybrid control is applied to the speed of an induction motor feed by three-level inverter. Based on the combination of the fuzzy logic and the sliding mode approach, this method has the advantage of combining the performances of the two types of controllers. The fuzzy logic confers a very appreciable flexibility to the reasoning which uses and makes it possible to take into account Imprecisions and uncertainties, The sliding mode is a controller for nonlinear systems with non-constant parameters; it leads to precision and robustness, and allows solving problems obtained by conventional control laws. To lift the stresses of external disturbance and makes the system more performing and more robust, the two controllers of fuzzy logic and sliding mode are combined.

Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, the cost of this energy is still too high to compete with traditional fossil sources. The yield of a wind turbine depends on three parameters: the power of the wind, the turbine power curve and the ability of the generator to respond to fluctuations in the wind. This article presented the MPPT of a wind turbine system equipped with an asynchronous generator has dual power under MatlabSimulink program, in the first time we simulated all the conversion chain with complete model of DFIG and vector control in second stepthen applied the extracted maximum power MPPT strategists, this command is effective and has several advantages it offered to kept the maximum power delivered to network despite all the parameter is change.

The development and the exploitation of renewable energies met a great growth these last years. Among these sources of energies, the windmill represents a significant potential not to replace existing energies, but to give solutions for the request, which always increases. The wind power can contribute with a significant part for the new sources of energy not emitting a gas for purpose of greenhouse (Andrianantenaina and al 2015). Currently, windmill system with variable speed based on the DFIG is widely used. Indeed, the DFIG presents more advantages. Several controls applied on the DFIG have been already proposed as (Andrianantenaina and al 2015; Razafinjaka and Andrianantenaina 2015,2016; Boualouch 2015) which give good performances. These last years, several researches are about the intelligent controllers such us fuzzy logic and neural network which have enjoyed great success in recent years for their robustness against disturbances which may affect process. Here, a hybridization of fuzzy logic with variable gain PI is proposed. After modeling the wind turbine and DFIG, we have established a vector control to control the active and reactive power control. The aim of this work is to present the performance and robustness of these controllers.

The development and the exploitation of renewable energies met a great growth these last years. Among these sources of energies, the windmill represents a significant potential not to replace existing energies, but to give solutions for the request, which always increases. The wind power can contribute with a significant part for the new sources of energy not emitting a gas for purpose of greenhouse (Andrianantenaina and al 2015). Currently, windmill system with variable speed based on the DFIG is widely used. Indeed, the DFIG presents more advantages. Several controls applied on the DFIG have been already proposed as (Andrianantenaina and al 2015; Razafinjaka and Andrianantenaina 2015,2016; Boualouch 2015) which give good performances. These last years, several researches are about the intelligent controllers such us fuzzy logic and neural network which have enjoyed great success in recent years for their robustness against disturbances which may affect process. Here, a hybridization of fuzzy logic with variable gain PI is proposed. After modeling the wind turbine and DFIG, we have established a vector control to control the active and reactive power control. The aim of this work is to present the performance and robustness of these controllers.

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.

To design an optimal fuzzy proportional-integral (PI) controller for the
variable speed wind turbine systems, a new differential evolution (DE)
algorithm approach is developed in this paper. We have investigated a fuzzy PI controller, in which fuzzy rules are applied to adapt the parameters of the PI controller founded on the error and its first-time derivative. The fuzzy PI controller's inputs and outputs are tuned using the DE optimization method. The superiority of the suggested (DE fuzzy PI) controller has been proved by comparing the results with (fuzzy PI) and only the PI controller applied to the wind turbine system. In comparison to a fuzzy controller with parameters selected by a human operator, the numerical validation results of the suggested approach (DE fuzzy PI) have shown good performance in terms of robustness, pursuit, and response time.

The diagnosis of wind energy conversion systems (WECS) turns out to be necessary because of their relatively high cost of operation and maintenance. Wind turbines are hard-to-access structures, and they are often located in remote areas. Therefore, a remote diagnosis (e-diagnosis) is required. This paper proposes an alternative approach for the e-diagnosis of a WECS based on the discrete wavelet transform (DWT) and frequency analysis of the aero generator stator currents. To validate this approach, real-time hardware in the loop (HIL) is used to simulate in real-time the mathematical model of the induction generator on the OPAL-RT OP5600 platform to generate the stator currents and the rotor speed. The DWT is applied to the current signal, to generate the DWT signal, which has a huge number of points that are not supported for direct transmission by the Arduino Mega RobotDyn because of its limited sample time. The absolute values of the DWT peak points (MDWT) are sent as point’s pac...

This paper has presented a device intended to fit in a wind mill based on a Doubly Fed Induction Generator connected to the grid. After a description of this device and its connection procedure, we have established a two-phase mathematical model of the DFIG. In order to control statoric active and reactive power exchanged between the DFIG and the grid, a vector-control strategy has been presented. Simulations have been investigated with two types of regulators: classical proportional-integral and polynomial RST based on pole-placement theory. The synthesis of the RST controller has been detailed. Simulations results have shown that performances are equivalent for the two controllers under ideal conditions (no perturbations and no parameters variations). The RST controller is more efficient when the speed is suddenly changed (which happens frequently in wind energy conversion systems) and is more robust under parameters variations of the DFIG (for example rotoric resistance in our st...

This paper presents the study of a variable speed wind energy conversion system using a Doubly Fed Induction Generator (DFIG) based on a Fuzzy sliding mode control (FSMC) applied to achieve control of active and reactive powers exchanged between the stator of the DFIG and the grid to ensure a Maximum Power Point Tracking (MPPT) of a wind energy conversion system. However the principal drawback of the sliding mode, is the chattering effect which characterized by torque ripple, this phenomena is undesirable and harmful for the machines, it generates noises and additional forces of torsion on the machine shaft. In order to reduce the chattering effect, the Sign function of sliding mode controller's discontinuous part is replaced by a fuzzy logic; we will have the fuzzy sliding mode controller (FSMC). The FSMC makes it possible to combine the performances of the two types of controllers (SMC and FLC) and eliminates the chattering effect. The proposed control algorithm is applied to a DFIG where the stator is directly connected to the grid and the rotor is connected to a three-level converter structure NPC to suppress low level harmonics, higher frequencies will be filtered out by the machine. Second goal of this paper is to extract a maximum of power; the rotor side converter is controlled by using a stator flux-oriented strategy. The decoupling created by the control between active and reactive stator power allows keeping the power factor close to unity. Simulation results show that the wind turbine can operate at its optimum energy for a wide range of wind speed. Both simulation and validation results show effectiveness of the proposed control strategy is in terms of power regulation. Moreover, the fuzzy sliding mode approach is arranged so as to reduce the chattering produced in the generated power that could lead to increased mechanical stress because of strong torque variations.

The present research paper deals with a study of a variable speed wind energy conversion system (VSWECS) based on a Doubly Fed Induction Generator (DFIG), the stator is directly connected to the grid and driven by climbed back-to-back converters. Direct control of DFIG with a variable structure based on an artificial neural network is presented. Artificial Neuronal Network ANN is proposed to improve performances and to substitute the classical PI regulators in the direct control of active and reactive powers of the DFIG. The performance of the approach has been tested and validated by simulation for different operating conditions. Simulation results and improvement of the behavior of the DFIG are presented, using Matlab/Simulink software.

Electricity consumption is increasing gradually and this trend will continue in the future. In addition, rapid network control systems using the resources offered by power electronics and control microelectronics have been recently studied and developed, and are currently in normal application for some, for others, in pilot applications or as prototypes. This paper attempts to show that these systems are referred to by the general acronym flexible alternative current transmission systems (FACTS) similarly dethroned the traditional systems while offering better solutions and solving the energy quality problem such as the hybrid system (unified power flow controller (UPFC), or universal phase shifter regulator (UPSR)) which opens up new perspectives for more efficient operation of networks by continuous and rapid action on the various parameters of the network (voltage, phase shift, and impedance); thus, the power transits will be better controlled and the voltages better held, which will make it possible to increase the stability margins or tend towards the thermal limits of the lines. In this work, we used a classic control (PI-decoupled) and others while offering more flexibility of control thanks to the development of strategies identification/control based on generalized predictive control (GPC) with neural network to ensure robust control with advanced algorithms.

This paper presents the doubly fed induction motor (DFIM) speed control using adaptive fuzzy logic PI (AFLPI) controller to give better dynamic performances. Before the advent of modern technology, integral proportional based current controller is usually used due to its simplicity. But, the performance of closed-loop control is largely influenced by this type of speed and torque controllers used, as long as the PI controllers suffer from tuning problem. To overcome the problem, a new technique AFLPI based speed controller for direct field oriented control fed DFIM to get fast speed response and to minimize the torque ripple. The application of this type of control is very satisfactory to replace the conventional PI controller and, even the fuzzy logic PI (FLPI) controller. The performance of the field oriented controlled DFIM drive has simulated at different operating conditions using the AFLPI controller and the obtained results are compared with FLPI controller and conventional P...

The small signal stability analysis of doubly fed induction (DFIG) generator in wind farm system is presented. The main purpose is to investigate the eigenvalues analysis of DFIG in case of the influence of a long distance transmission line from grid system. The DFIG dynamic model and the improvement of initialize technique in load flow calculation of DFIG are analyzed. The simulation result shows the eigenvalues analysis results of DFIG based on many cases of transmission distance when the load variation. As the result, the optimal location on generator from the grid system is confirmed.

This paper presents small signal stability analysis of a doubly fed induction generator (DFIG) based wind farm including series dynamic braking resistor (SDBR) connected at the stator side. A detailed mathematical model of wind turbine, DFIG machine and converters and SDBR is presented in this paper to derive the complete dynamic equations of the studied system. Small signal stability of this system is carried out by modal and sensitivity analysis, participation factors and eigenvalue analysis. Finally, this paper presents an analysis of the dynamic behavior of DFIG based wind farm under voltage dip condition with and without SDBR.

Advances in Fuzzy Systems has retracted the article titled “Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine” [1].The article was previously published as “Loukal Keltoum, Benalia Leila, “Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM),” International Journal of Artificial Intelligence, Vol 4, No 4, 2015. http:// iaescore.com/journals/index.php/IJAI/article/view/6125 [2]. The authors say they did not approve final publication in International Journal of Artificial Intelligence and they do not support the retraction. The IJAI Editor-in-Chief said this was the responsibility of journal staff, but the journal and publisher did not respond to our queries.

In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic control method is applied in the estimator structure instead of PI controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy controller shows more robustness against the variations of machine parameters.

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.

The objective of this paper is to study the dynamic response of the wind energy conversion system (WECS) based on the Doubly Fed Induction Generator (DFIG). The DFIG rotor is connected to the grid via a converter. The active and reactive power control is realized by the DFIG rotor variables control, using the field oriented control (FOC). The vector control of DFIG is applied by the use of tow regulators PI and the neural network regulator (NN). The generator mathematical model is implemented in Matlab/ Simulink software to simulate a DFIG of 1.5 MW in order to show the efficiency of the performances and robustness of the studied control systems. The simulation obtained results shows that the robustness and response time of the neural network regulator is better than those obtained by the PI classical regulator.

This work is dedicated to the study of an improved direct torque control of the doubly fed induction motor (DFIM). The control method adopts direct torque control 'DTC' because of its various advantages like the ease of implementation which allows a good performance at transient and steady state without PI regulators and rotating coordinate transformations. To do this, the modeling of the motor is performed. Subsequently, an explanation of the said command is spread out as well as the principle of adjusting the flux and the electromagnetic torque according to the desired speed. Then, the estimation method of these two control variables will be presented as well as the adopted switching table of the hysteresis controller model used based on the model of the multilevel inverters. Finally, the robustness of the developed system will be analyzed with validation in Matlab/Simulink environment to illustrate the performance of this control.

The main objective of this paper is to continue the development of activities of basic and applied research related to wind energy and to develop methods of optimal control to improve the performance and production of electrical energy from wind. A new control technique of Double fed induction generator for wind turbine is undertaken through a robust approach tagged nonlinear sliding mode control (SMC) with exponential reaching law control (ERL). The SMC with ERL proves to be capable of reducing the system chattering phenomenon as well as accelerating the approaching process. A nonlinear case numerical simulation test is employed to verify the superior performance of the ERL method over traditional power rate reaching strategy. Results obtained in Matlab/Simulink environment show that the SMC with ERL is more robust, prove excellent performance for the control unit by improving power quality and stability of wind turbine.

The objective of this paper is to study the dynamic response of the wind energy conversion system (WECS) based on the Doubly Fed Induction Generator (DFIG). The DFIG rotor is connected to the grid via a converter. The active and reactive power control is realized by the DFIG rotor variables control, using the field oriented control (FOC). The vector control of DFIG is applied by the use of tow regulators PI and the neural network regulator (NN). The generator mathematical model is implemented in Matlab/ Simulink software to simulate a DFIG of 1.5 MW in order to show the efficiency of the performances and robustness of the studied control systems. The simulation obtained results shows that the robustness and response time of the neural network regulator is better than those obtained by the PI classical regulator.

The objective of this paper is to study the dynamic response of the wind energy conversion system (WECS) based on the Doubly Fed Induction Generator (DFIG). The DFIG rotor is connected to the grid via a converter. The active and reactive power control is realized by the DFIG rotor variables control, using the field oriented control (FOC). The vector control of DFIG is applied by the use of tow regulators PI and the neural network regulator (NN). The generator mathematical model is implemented in Matlab/ Simulink software to simulate a DFIG of 1.5 MW in order to show the efficiency of the performances and robustness of the studied control systems. The simulation obtained results shows that the robustness and response time of the neural network regulator is better than those obtained by the PI classical regulator.