Krim Yazid

Bio: Krim Yazid is an academic researcher from University of Science and Technology Houari Boumediene. The author has contributed to research in topics: Vector control & Induction motor. The author has an hindex of 7, co-authored 25 publications receiving 127 citations. Previous affiliations of Krim Yazid include University of the Sciences.

Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic

Abstract: Display Omitted An hybrid approach for vector control of the three-phase induction motor is proposed.The proposed approach combines in a different manner that existing in literature the two techniques: the artificial neuronal network (ANN) and the fuzzy logic (FL).The performances of the proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated by simulations; and performances proofs are further verified by comparative results to others controls in different operating conditions.The results show the feasibility and good performance obtained by the proposed control: improved response time and mostly enhanced the robustness of the system. Furthermore, it reinforces the principle of vector control, procuring a good decoupling between the electromagnetic torque and rotor flux. Nowadays, the microcomputer performs calculations at an incredibly high rate of billions of instructions per second. That represents an exponential increase in the processing speed since the early days of the computer development, eventhough such growth did not show complex reasoning that even the simple biological organisms can make. The artificial intelligence techniques as an attempt to work about those limitations, are a promising alternative.Each intelligent technique has its particular strengths and weaknesses and cannot be universally implemented to any problem. Mixed together, these techniques can improve the solutions quality and allow application to various tasks. It is the reason why the AI is used increasingly in order to solve complex problems in engineering. Where, it is still necessary to make progress in the controller tuning.The idea proposed in this paper is simple and original. It is the result of a study that compared the performances of two controls based on the artificial intelligence techniques: the artificial neural networks and the fuzzy logic. The control proposed in this paper combines in a different manner these two techniques in the form of a hybrid control. The aim is to benefit from performances of each of these techniques, by using them in the same control block at the most suitable place.The performances of the this proposed hybrid control; applied to the three-phase induction motor supplied by voltage source inverter; are investigated and compared to those obtained from the controls based on artificial neural networks; fuzzy logic and conventional techniques. The results of simulation show the feasibility and the good performances achieved by the proposed control.

Sensorless indirect vector control of an induction motor by ANNs observer and EKF

Abstract: This paper describes the application of ANNs observer based on the extended Kalman filter (EKF) for rotor resistance, Mutual inductance and rotor speed estimation of an induction motor, in order to overcome the constraint of adjustment of the covariance matrices and to improve the estimate of these last three parameters. It is an observer for non-linear systems; it is applied to the indirect field oriented control. The magnetic saturation, the operating temperature and difficulties in using sensors for speed measurement are one of the sources of parameters variations. The indirect field oriented control (IFOC) is highly sensitive to these variations and to the noise. To fix this problem, the artificial neural networks (ANNs) observer based on the extended Kalman filter (EKF) is applied to sensorless parameters estimation of an induction motor. The general structure of the EKF is reviewed and the various systems vectors and matrices are defined. The elements of the covariance matrices are properly selected. The EKF associated to the neural network (EKF-ANNs) trained off line algorithm are used to estimate the rotor resistance, the mutual inductance and the rotor speed. By including these parameters as states variables, the EKF equations are established from a discrete two-axis model of the three-phase induction motor. The results obtained with the proposed observer are more efficient than the results obtained with classical EKF.

Fuzzy logic-based direct torque control for induction machine drive

Abstract: Direct torque control (DTC) of induction machines presents an acceptable tracking scheme for both electromagnetic torque and stator flux. However, conventional DTC scheme, based on hysteresis comparators and the switching table, suffers from large torque and flux ripples, requiring high switching frequency operation for the voltage source inverter. In this paper, a modified DTC scheme based on fuzzy logic rules is proposed. A fuzzy controller (FC) is designed in order to give the appropriate inverter voltage vector. The FC could judge the deviation degree of the torque and flux errors to select the optimum voltage vector according to the fuzzy logic inference. The effectiveness of the proposed FC-based DTC for induction machine (IM) drive is verified at several operating conditions and highlighted by comparing to the conventional DTC. The obtained results illustrate low switching frequency, considerable ripples mitigation of torque, flux and the stator currents and improving the system performance.

An effective identification of rotor resistance for induction motor vector control

Abstract: Nowadays, vector control induction motor have gained wide acceptance in high performance applications. Crucial to the success of the indirect vector control is the knowledge of the instantaneous position of the rotor flux. This requires a priori knowledge of rotor time constant which varies with rotor temperature. So, the variations of rotor circuit time constant can cause significant performance deterioration if no means for compensation or identification is applied. In this paper, an approach to estimating rotor resistance variations for induction motor vector control via temperature is proposed. In essence, the approach uses sensors to detect temperature, which is then used to determine the correct values of rotor resistance. From results, it is verified that the proposed approach can overcome the problem of performance degradation due to rotor resistance variations. The method is applicable for the design of a gain scheduling ac drives to optimize their performance.

Parameters identification of a brushless doubly fed induction machine using pseudo-random binary signal excitation signal for recursive least squares method

Abstract: Accurate brushless doubly fed induction machine (BDFIM) control is considered as a good alternative to traditional generators. Therefore, the knowledge of the parameters accuracy is necessary to design high-performance drives. In this study, an offline identification method to identify the BDFIM parameters is presented. This study investigates the input signal effects on the accuracy of the identified parameters. The similarity between BDFIM frequency model and the standard induction machine frequency model has been verified by comparing the step responses of the corresponding transfer functions. The obtained results would be a motivation for online identification techniques and BDFIM field-oriented control strategies. The proposed identification method has been validated by performing experimental results.

Applications of fuzzy logic in renewable energy systems – A review

Abstract: In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

A review on sensorless techniques for sustainable reliablity and efficient variable frequency drives of induction motors

Abstract: Variable frequency drives (VFDs) can provide reliable dynamic systems and significant savings in energy usage and costs of the induction motors (IMs). Sensorless controlled IM drives have advantages in terms of efficiency enhancement and energy savings for critical applications such as electric vehicles, high performance machine tools, fans, compressors, etc. IM drives without having speed sensors or optical encoders mounted at the motor shaft are attractive because of their lower cost and higher reliability. When mechanical speed sensor is removed, the rotor speed information is estimated using the measured quantities of stator voltages and currents at the IM terminals. This paper highlights the sensorless techniques applied to the IM drives for sustainable reliability and energy savings. Overview on the IM mathematical model is briefly summarized to establish a physical basis for the sensorless schemes used. Further, the different types of IM-VFDs are presented in the paper. The main focus of this review is on the sensorless estimation techniques which are being applied to make IM-VFDs more effective during wide speed operations including very-high and very-low speed regions.

Double Hidden Layer Output Feedback Neural Adaptive Global Sliding Mode Control of Active Power Filter

Abstract: In this paper, a self-regulated double hidden layer output feedback neural network (DHLFNN) is presented to control an active power filter (APF) system as a current controller, which is conducive to the improvement of the response characteristic and power quality. First, a global sliding mode controller is introduced because it is effective in achieving overall robustness during the system response. A new output feedback neural structure that has two hidden layers is proposed to make the parameters adaptively adjust themselves and stabilize to their best values. A higher accuracy and stronger generalization ability can be also obtained by reducing the number of network weights and accelerating the network training speed owing to the strong fitting and presentation ability of two-layer activation functions. Furthermore, the designed feedback loops of the neural network play a significant role in possessing associative memory and rapid system convergence. This proposed double hidden layer output feedback neural based global sliding mode controller is simulated on the model of APF and the results show the excellent static and dynamic properties. Experimental results under three cases and comparisons are provided using a fully digital control system to validate the superior performance of the proposed DHLFNN controller.

Research on Robust Performance of Speed-Sensorless Vector Control for the Induction Motor Using an Interfacing Multiple-Model Extended Kalman Filter

Abstract: The interfacing multiple-model extended Kalman filter (IMM-EKF) is proposed here as a modification of the extended Kalman filter (EKF). In this algorithm, two multiple-model EKF groups are built, one group is the optimum model, and the other is the noise model. Each model group is created by multiple models, and it will get good performance at stable state and robust ability when disturbance occurred. The algorithm gets the estimation value by mixing the outputs of the different model in different weightings, and the calculation of weightings is researched. Whether the IMM-EKF can give better estimation performances and robust ability than the EKF for speed estimation of induction machines is explored in this paper. Via simulations and experiments, estimated error and the change of flux linkage by disturbance based on the IMM-EKF and EKF is compared. The simulation results show that the IMM-EKF has the better estimation performance of antigross error than the EKF.

Brushless doubly-fed machines : opportunities and challenges

Abstract: The brushless doubly-fed machine(BDFM) is a family of multiport electric machines with two ac electrical ports and a common mechanical port. Different from the conventional singly-fed machines whose synchronous speed is solely determined by a single supply frequency and the actual pole pair number, the BDFM has two supply frequencies and two different pole pair numbers to control the rotor speed. By the two accessible electrical ports, all BDFMs are endowed with more degrees of freedom for speed and power control, inherent fault-tolerant capability and high reliability. The BDFM in its broad sense has been extensively investigated as a promising alternative to the conventional slip-ring doubly-fed induction machine(DFIM) during the past decades, for both limited and wide speed range applications. This paper presents a new theoretical framework of the BDFM within which all topological variants can be closely linked by the similarities in working principle. The individualities of each machine topology are presented first, followed by the commonalities such as the modeling techniques, modes of operation, design considerations and control strategies. The challenges are identified and highlighted based on recent developments and possible opportunities are predicted considering the unique nature of this special AC machine type.