A Multi-Objective Evolution Programming Method for Feeder Reconfiguration of Power Distribution System

Using soft computing for solving distribution reconfiguration problems were studied for many years. Genetic algorithm (GA) is one of the most popular technologies in the soft computing area for solving distribution system problems. However, due to the radial structure of power distribution system, traditional GAs may encounter some difficulties when searching for the optimal solution. Evolutionary programming (EP) was also being used to solve some distribution system problems, for example, loss minimization, service restoration, capacitor placement and many others. Hence, the EP is applied in this paper in order to overcome the weakness of traditional GAs (Fudou et. al, (1997); Miranda et al., (1994); Nara et al., (2003); Ying-Tung Hsiao, (2004), Back et al., (2004); Ying-Tung Hsiao and Ching-Yang Chien, 2000). One of the differences between GA and EP is that the weighting of chromosomes is used for selection operator. The weighting calculation of this paper is based on the characteristics of feeder losses and load balancing on distribution feeders. The results show that the proposed EP with adapted weight calculation performs better than traditional GAs