Double-stator switched reluctance machines benefit from a high torque density and a low radial force level in comparison with conventional switched reluctance machines resulting in a lower vibration and acoustic noise. Therefore, they are suitable candidate for automotive applications. However, torque pulsation which is also a source for vibration is still remained and should be alleviate by dimension optimization of the machine. This paper presents a design optimization of a double-stator switched reluctance machine for improving the magnetic torque quality of the machine. For this purpose finite element method along with response surface methodology is used to optimize three parameters of the machine to maximize torque quality factor i.e. the average torque to torque ripple ratio in the machine. Genetic algorithm method is also employed as an optimization tool. The aim of optimization is to maximize the ratio of average torque to torque ripple. Finite element results are presented to verify the optimization method.
Static Var Compensator (SVC) is one of FACTS devices which is used in power systems in order to have a better voltage quality. The fluctuations of voltage and current of the grid connected to electric arc furnace causes power quality problems in the network. SVC, as a fast reactive power control equipment, plays an important role in improving the voltage profile and the transient dynamics of the system arising from the nature of the arc. In this paper the effect of implementing SVC in Mobarakeh Steel Company, for mitigating the voltage transient of power system which is connected to an electric arc furnace, is studied. Then the coefficients of the SVC PI controller are optimized using genetic algorithm and the simulation results are presented.
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