Ce travail présente des techniques de commande robustes dédiées au système éolien basé sur la machine asynchrone à double alimentation sans balais (MADASB) entrainée par une turbine à calage variable des pales. Le stator de puissance de la machine est directement relié au réseau électrique ; par contre le stator de commande est alimenté par un convertisseur bidirectionnel. Les MADASB promettent des avantages significatifs pour les systèmes de conversion d’énergie éolienne en raison de leurs faibles coûts et une plus grande fiabilité par rapport aux machines asynchrones à double alimentation classiques (MADA). L’idée principale consiste à appliquer différentes techniques de commande pour le contrôle indépendant des puissances active et réactive générées par la MADASB découplée par la technique de commande vectorielle. Il s’agit particulièrement de la commande par logique floue et des commandes par mode glissant d’ordre un et de second ordre basée sur l’algorithme du Super-Twisting, la commande par retour d’état et la commande par backstepping. Une étude comparative relative aux performances obtenues par les commandes proposées est menée. Les résultats de simulation montrent que ces méthodes hiérarchisées, possèdent de grandes performances dans le contrôle de tels systèmes en termes de poursuite de la référence, de découplage, de temps de réponse et de la qualité du courant.

Cette thèse a pour but d’apporter une contribution au contrôle direct des puissances d’une chaine de conversion d’énergie éolienne à base d’une GADA en faisant varier la vitesse de l’éolienne en réponse au changement de la vitesse du vent afin d’optimiser l’énergie éolienne extraite et de concevoir une commande robuste face aux incertitudes paramétriques. Plusieurs structures ont été développées ces structures concernant la combinaison de déférentes techniques de commandes pour aboutir à des meilleurs résultats. Dans ce contexte une commande adaptative pour le contrôle des puissances de la GADA a été présentée, avec cette technique les oscillations des puissances sont réduites, la réponse dynamique du système a été améliorée. Dans ce travail, nous avons aussi développé une autre technique qui réduit les oscillations des puissances, cette technique que nous avons appelé DPC basée sur le mode glissant d’ordre supérieur. L’ensemble des résultats obtenus a montré satisfaction quant aux performances atteintes par le système. Celles-ci sont traduites par la robustesse de la commande vis-à-vis des incertitudes paramétriques de la GADA

In this paper, a high-performance indirect field-oriented controlled dual Induction Motor (IM) drive fed by a single inverter using type-2 fuzzy logic control will be presented. At first, the mathematical model of the IM is implemented in the d-q reference frame. Then, the speed control of the Dual Induction Motor (DIM) operating in parallel configuration with Indirect Field Oriented Control (IFOC) using PI and type-2 Fuzzy Logic Controller (T2-FLC) will be presented. For the control of this system, a DC supply and a Space Vector Pulse Width Modulation (SVPWM) voltage source inverter are introduced with constant switching frequency. Also, the performance of T2-FLC, which is based on the IFOC, is tested and compared to those achieved using the PI controller. The simulation results demonstrate that the T2-FLC is more robust, efficient, and has superior dynamic performance for traction system applications.

In this paper, a high-performance indirect field-oriented controlled dual Induction Motor (IM) drive fed by a single inverter using type-2 fuzzy logic control will be presented. At first, the mathematical model of the IM is implemented in the d-q reference frame. Then, the speed control of the Dual Induction Motor (DIM) operating in parallel configuration with Indirect Field Oriented Control (IFOC) using PI and type-2 Fuzzy Logic Controller (T2-FLC) will be presented. For the control of this system, a DC supply and a Space Vector Pulse Width Modulation (SVPWM) voltage source inverter are introduced with constant switching frequency. Also, the performance of T2-FLC, which is based on the IFOC, is tested and compared to those achieved using the PI controller. The simulation results demonstrate that the T2-FLC is more robust, efficient, and has superior dynamic performance for traction system applications.

In this paper, a high-performance indirect field-oriented controlled dual Induction Motor (IM) drive fed by a single inverter using type-2 fuzzy logic control will be presented. At first, the mathematical model of the IM is implemented in the d-q reference frame. Then, the speed control of the Dual Induction Motor (DIM) operating in parallel configuration with Indirect Field Oriented Control (IFOC) using PI and type-2 Fuzzy Logic Controller (T2-FLC) will be presented. For the control of this system, a DC supply and a Space Vector Pulse Width Modulation (SVPWM) voltage source inverter are introduced with constant switching frequency. Also, the performance of T2-FLC, which is based on the IFOC, is tested and compared to those achieved using the PI controller. The simulation results demonstrate that the T2-FLC is more robust, efficient, and has superior dynamic performance for traction system applications.

In this paper, a direct vector control using fuzzy logic controller with adaptive gain for a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) is presented. The performance of fuzzy controllers is characterized by unsatisfactory performance: (wide overshoot, excessive oscillations and sensitivity to parametric variations). We propose a robust method, where the control gain will be continually adapted with the use of a set of fuzzy rules; we only consider the gain adaptation of the command. I mean the value of the gain will be determined by a rule base defined by the error and the variation of the error. Finally, the control of the active and reactive powers using a fuzzy logic controller with adaptive gain is simulated using software Matlab/Simulink, studies on a 1.5 MW DFIG wind generation system compared with the conventional fuzzy logic controller. Performance and robustness results obtained are presented and analyzed. KEY WORDS Wind energy conversion system ; Vector control ; Fuzzy logic controller ; Adaptive fuzzy logic controller.

In this paper, a direct vector control using fuzzy logic controller with adaptive gain for a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) is presented. The performance of fuzzy controllers is characterized by unsatisfactory performance: (wide overshoot, excessive oscillations and sensitivity to parametric variations). We propose a robust method, where the control gain will be continually adapted with the use of a set of fuzzy rules; we only consider the gain adaptation of the command. I mean the value of the gain will be determined by a rule base defined by the error and the variation of the error. Finally, the control of the active and reactive powers using a fuzzy logic controller with adaptive gain is simulated using software Matlab/Simulink, studies on a 1.5 MW DFIG wind generation system compared with the conventional fuzzy logic controller. Performance and robustness results obtained are presented and analyzed. KEY WORDS Wind energy conversion system ; Vector control ; Fuzzy logic controller ; Adaptive fuzzy logic controller.

In this paper, a direct vector control using fuzzy logic controller with adaptive gain for a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) is presented. The performance of fuzzy controllers is characterized by unsatisfactory performance: (wide overshoot, excessive oscillations and sensitivity to parametric variations). We propose a robust method, where the control gain will be continually adapted with the use of a set of fuzzy rules; we only consider the gain adaptation of the command. I mean the value of the gain will be determined by a rule base defined by the error and the variation of the error. Finally, the control of the active and reactive powers using a fuzzy logic controller with adaptive gain is simulated using software Matlab/Simulink, studies on a 1.5 MW DFIG wind generation system compared with the conventional fuzzy logic controller. Performance and robustness results obtained are presented and analyzed. KEY WORDS Wind energy conversion system ; Vector control ; Fuzzy logic controller ; Adaptive fuzzy logic 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.

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.

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.

This paper presents a novel direct rotor flux oriented control with online estimation of magnetizing current and magnetizing inductance applied to self-excited dual star induction generator equipping a wind turbine in remote sites. The induction generator is connected to nonlinear load through two PWM rectifiers. The fuzzy logic controller is used to ensure the DC bus voltage a constant value when changes in speed and load conditions. In this study, a performance comparison between the conventional approach and the novel approach is made. The proposed control strategy is validated by simulation in Matlab/Simulink.

This paper presents a novel direct rotor flux oriented control with online estimation of magnetizing current and magnetizing inductance applied to self-excited dual star induction generator equipping a wind turbine in remote sites. The induction generator is connected to nonlinear load through two PWM rectifiers. The fuzzy logic controller is used to ensure the DC bus voltage a constant value when changes in speed and load conditions. In this study, a performance comparison between the conventional approach and the novel approach is made. The proposed control strategy is validated by simulation in Matlab/Simulink.

This paper presents a novel direct rotor flux oriented control with online estimation of magnetizing current and magnetizing inductance applied to self-excited dual star induction generator equipping a wind turbine in remote sites. The induction generator is connected to nonlinear load through two PWM rectifiers. The fuzzy logic controller is used to ensure the DC bus voltage a constant value when changes in speed and load conditions. In this study, a performance comparison between the conventional approach and the novel approach is made. The proposed control strategy is validated by simulation in Matlab/Simulink.

Les énergies renouvelables prennent ces dernières années un axe d’investigation pour les chercheurs. Pour cette raison, notre étude est consacrée à l’application des différentes commandes non linéaires à la génératrice asynchrone double alimentée (GADA) intégrée dans un système de conversion de l’énergie éolienne. En premier lieu on présente l’application de la commande vectorielle associée à un système éolien. Pour raison d’amélioration des performances, des commandes avancées de type Mode Glissant (MG) et Backstepping (Back) sont appliquées à la GADA-éolienne afin d’assurer un découplage entre les puissances active et réactive pour des vitesses fixe et variable avec des performances souhaitées. La stratégie MPPT (Maximum Power Point Track) pour extraire le maximum de puissance pendant la conversion est développée. Aussi, la technique SVM (Space Vector Modulation) est appliquée. L’étude comparative des différentes commandes étudiées à travers les résultats des simulations montre une amélioration significative des performances des contrôleurs non linéaires, Backstepping (Back) et Mode Glissant (MG) proposés par rapport au contrôleur vectoriel en termes de réponse dynamique, de rejet des perturbations et des variations paramétriques

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.

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.

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.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.