PROJECT TITLE :
A Fast Nondominated Sorting Guided Genetic Algorithm for Multi-Objective Power Distribution System Reconfiguration Problem
Distribution system reconfiguration (DSR) could be a multi-objective, nonlinear drawback. This paper introduces a new, fast, nondominated sorting genetic algorithm (FNSGA) for the aim of solving the DSR drawback in traditional operation by satisfying all objectives simultaneously with a relatively tiny variety of generations and relatively short computation time. The objectives of the matter are to attenuate real power losses and improve the voltage profile and load balancing index with minimum switching operations. Instead of generating several ranks from the nondominated set of solutions, this algorithm deals with solely one rank; then the foremost appropriate answer is chosen in line with the operator's desires. If there is no preference and every one objectives have the same degree of importance, the simplest solution is decided by simply considering the total of the normalized objective values. Conjointly, a guided mutation operation is applied rather than a random one to hurry up convergence. Radial system topology is happy using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of every topology. To check the algorithm, it absolutely was applied to two widely studied take a look at systems and a true one. The results show the potency of this algorithm as compared to alternative methods in terms of achieving all the goals simultaneously with cheap population and generation sizes and without using a mutation rate, which is sometimes drawback-dependent.
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