Introduction:
This project discussed about how Improved Genetic Algorithm addresses the network Reconfiguration problems in distribution systems. Efficiency of distribution systems can be achieved by minimizing the losses, which in turn responsible to increase voltage stability and load balancing. The main goal of this project is to design a methodology for network reconfiguration in order to minimize losses and improve reliability.
Overview:
Both analytical methods and Monte Carlo simulation methods are used to improve the distribution system’s reliability. In the past years, branch and bound type optimization techniques are used to reduce the losses in distribution systems. Conventional genetic algorithm is also used to reduce the losses in distribution systems reconfiguration, but it’s have less convergence speed. To overcome the problems in conventional genetic algorithm many research has taken place and new improved genetic algorithm is emerged from these researches. IGA introduced black list of infeasible solutions, crossover dynamic adaption and a two- termination criterion to improve efficiency and to reduce losses in distribution systems. During the optimization process, IGA uses chromosome with a small length. IGA also uses suitable coding and decoding techniques for its processes. Crossover and mutation probabilities severely affect the performance and behavior of Genetic algorithm. The termination criterion in IGA depends on either maximum number of generations or mentioned convergence threshold. During optimization process, blacklist identifies all infeasible solutions, which in turn responsible to increase the efficiency in distribution systems.
Conclusions
Network configuration used improved genetic algorithm (IGA) approach to improve the efficiency and Reliability in distribution systems. To predict the reliability of the network configurations, a Monte Carle simulation method is used. Coding techniques used by IGA is responsible for less computation, when compared to that of remaining techniques. One of the important characteristics of IGA is the creation of blacklist which identifies all infeasible solutions during optimization process. This blacklist is useful to increase the efficiency in distribution systems.
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