عنوان مقاله :
كنترل ولتاژ و توان راكتيو در شبكه هاي توزيع با در نظر گرفتن اثر توليدات پراكنده
عنوان به زبان ديگر :
Volt/Var Control in Distribution networks regarding Distributed Generation
پديد آورندگان :
نيكنام ، طاهر نويسنده ,
كليدواژه :
Distributed generation , Voltage and reactive power control , Ant Colony Optimization (ACO) , Swarm Optimization , توان راكتيو , شبكه توزيع , توليدات پراكنده , صنعت برق , كنترل ولتاژ
چكيده لاتين :
Optimal management of voltage and reactive power is one of the most important problems in distribution networks. The purpose of this management is to decrease losses, reform and improve voltage profile in distribution networks with the use of changing taps of under load tap-changers and capacitors in order to change daily loads by distribution offices. Nowadays problems such as environment pollution, restructuring in power system and problems in new transmission establishment along with technology improvement result in more uses of small generating units like micro turbines, wind turbines, photo voltaic, etc. The aim of this paper is to present a method for controlling voltage and reactive power in distribution networks with regard to the effects of Dispersed Generations for daily variations of loads. Objective function contains sum of network losses, cost of reactive power generadon and voltage variations in all buses relative to rated values. Because optimal management of voltage and reactive power is a nonlinear mix-integer optimization problem, it is difficult to solve these problems with classic and elementary methods. Since evolutionary methods are independent of the type of objective function and state variables, they can be used for solving these problems. But most crucial difficulty about evolutionary methods is slow rate of convergence. In other words, these methods require more time to converge. In this paper a new evolutionary method based on Ant Colony Algorithm has been applied. This method has higher rate of convergence and better response in comparison with methods such as Genetic Algorithm. The results of this method tested on an IEEE network containing 34 buses for various states of daily load have been offered.
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