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

[1]  Vladimiro Miranda,et al.  Genetic algorithms in optimal multistage distribution network planning , 1994 .

[2]  Y. Fukuyama,et al.  Comparative study of modern heuristic algorithms to service restoration in distribution systems , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[3]  Men-Shen Tsai,et al.  Comparison of genetic algorithm reproduction methods for distribution system loss minimization , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[4]  C. Chen,et al.  The interactive step trade-off method (ISTM) for multiobjective optimization , 1990, IEEE Trans. Syst. Man Cybern..

[5]  Yoshikazu Fukuyama,et al.  A genetic algorithm for network reconfiguration using three phase unbalance load flow , 2000 .

[6]  Ying-Tung Hsiao,et al.  Multiobjective evolution programming method for feeder reconfiguration , 2004 .

[7]  Y.-T. Hsiao,et al.  Enhancement of restoration service in distribution systems using a combination fuzzy-GA method , 2000 .

[8]  Men-Shen Tsai,et al.  Development of an object-oriented expert system for multi-period load transfer , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[9]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[10]  Koichi Nara,et al.  Network reconfiguration for loss minimization and load balancing , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[11]  Ying-Tung Hsiao.  Multiobjective evolution programming method for feeder reconfiguration , 2004, IEEE Transactions on Power Systems.

[12]  Hans-Paul Schwefel,et al.  Evolutionary Programming and Evolution Strategies: Similarities and Differences , 1993 .