This paper proposed a new converter topology to drive induction motors. The proposed topology can boost the DC link input voltage in case of DC link voltage shortage without any additional converter. This converter employs two additional switches and one capacitor in each phase compared to conventional converters. Moreover, this new topology brings additional advantages such as less torque and current ripples and lower switching loss. After a discussion of the basic principles of operation, the dynamics of the converter is developed for control purposes.Simulation results are given to illustrate the associated advantages of the proposed converter.

In this paper, simultaneous estimation of the rotor speed and time constant estimation for a voltage source inverter–fed induction motor drive is discussed. Application of the Model Reference Adaptive System (MRAS) on the vector controlling of an induction motor drive in the rotor-flux-oriented reference frame is examined. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance is also designed.

In this paper, a neural network model reference adaptive system speed observer is designed, which can be used inspeed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In otherwords, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Thenequations of the reference model and adaptive model have been extracted. Existence of the speed-dependent functionsin the reference model causes error in speed estimation. In order to reduce error, the reference model equations areupdated unlike the standard MRAS method. The adaptive model equations have also been discrete and they have beenrewritten so as to be represented by a linear neural network (ADALINE). On this basis, the so-called back propagationhas been used to compute online, in recursive form, the machine linear speed. Finally, the estimated speed is used formotor speed control. The simulations show the efficiency of the method.

In most industrial zones, electric energy is one of the most important energy sources. Since electrical motors are the main energy consumers of industrial factories, consumption optimization in these motors can be considered as a main option related to energy saving. One very effective way to reduce the consumption of these equipment is to use a motor speed controllers or drives. Since the loss of inductive motor has a direct relationship with motor flux, in this paper, the rotor flux vector control has been used. Due to the strength of fuzzy controllers in load failure and noise generation states, this controller has been used to adjust the drive speed. Two fuzzy logic inputs including speed error and speed variation derivative, and a fuzzy output, motor reference torque (Te*) are estimated. The genetic optimization algorithm has been used in order to improve the Efficiency and reduce the losses. As such, the drive performance in GA and Fuzzy-Genetic (FG) states is reviewed and the simulation results are presented. Finally, the obtained results in this paper have been compared to the results of FOC inductive motor with PI controller and without optimization. It can be seen that when FG method is employed, the results show a higher performance and losses are reduced up to almost 40 to 50% in different loads, and the amount of input power is also reduced up to approximately 30%.