فهرست مطالب seyed saeed fazel

Air pollution and energy shortage as serious crises , made researchers and industrialists trying to find suitable solutions with the aim of energy management. Electric transportation loads ( especially , electric vehicles and urban metro systems ) , are considered as the major consumers of energy for the electric network . Since the mentioned vehicles, have time overlap with the peak demand of main electric network at the peak time consumption , the feeding procedure has negative impacts on the upstream grid . One of the best solutions is reusing the huge regenerative energy of braking trains . The regenerative energy could be taken to consideration as a distributed generation in electric transportation system as well as the distributed generations of power systems like solar energy resources . Therefore , in this paper an integrated configuration comprised of the urban metro system , charging station for electric vehicles , photovoltaic panels , and energy storage as an interface for energy exchange is introduced to alleviate the problems . The priorities of power consuming , supplying, charging or discharging are defined by a smart energy management system . This system imposes the economic and technical constraints to power flows . By considering this method the profitable consequences are imminent . Supplying the loads by the smart configuration leads to energy management and consequently the reduction of the operation and infrastructure expenses and decreasing the negative impacts on the upstream network by optimizing the model of energy consumption from economic and technical points of view .

Although, Single-Sided Linear Induction Motor (SLIM) utilization has increased in railway applications due to their numerous advantages in comparison to Rotational Induction Motors (RIM), there are some sophistication in their mathematical models and electrical drive. This paper focuses on the problems of SLIM modeling, with assuming end-effect on the basis of Field Oriented Control (FOC) as a drive control method. To do so, two dynamic models of SLIM are described and investigated. To evaluate their technical characteristics (i.e., thrust, torque, current, and speed) and also motor performance, a comparison is realized for synchronous and stationary reference frames in both dynamic models. Further, the disparities of these two dynamic models and FOC operation in the two reference frames are discussed. Finally, the validity of the control method accuracy is verified for each model by simulation results.

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