Optimal distribution network reconfiguration using fuzzy interaction and MPSO algorithm

The Distribution Feeder Reconfiguration (DFR) is among the most well-known reinforcement methods to improve the radial distribution capabilities. Optimal management of DFR problem can yield many advantages such as power loss reduction, load balance improvement, voltage profile enhancement, etc. Therefore, the main purpose of this paper is to investigate the positive effect of the DFR strategy on the network performance. In this regard, a new fuzzy-based interactive method based on modified particle swarm optimization (PSO) algorithm is suggested to study the multi-objective DFR problem deeply. The objective functions to be investigated are total active power losses, voltage deviation and load balance. Also, a fuzzy based interactive method is utilized to satisfy all the objective functions sufficiently. The idea of the fuzzy-based interactive method will let the operator to determine the satisfying degree of each objective function. The feasibility and effectiveness of the proposed method is examined on the 32-bus IEEE radial distribution system.

[1]  S. K. Basu,et al.  A new algorithm for the reconfiguration of distribution feeders for loss minimization , 1992 .

[2]  Taher Niknam,et al.  A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION , 2009, Cybern. Syst..

[3]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[4]  Young-Jae Jeon,et al.  Network reconfiguration in radial distribution system using simulated annealing and Tabu search , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[5]  Taher Niknam,et al.  Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants , 2012 .

[6]  S. Carneiro,et al.  A new heuristic reconfiguration algorithm for large distribution systems , 2005, 2006 IEEE Power Engineering Society General Meeting.

[7]  Ming-Tong Tsay,et al.  Distribution feeder reconfiguration with refined genetic algorithm , 2000 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Taher Niknam,et al.  Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators , 2008 .

[10]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[11]  I. Mareels,et al.  An efficient brute-force solution to the network reconfiguration problem , 2000 .

[12]  W.-H.E. Liu,et al.  Distribution feeder reconfiguration for service restoration and load balancing , 1997 .

[13]  P. Bastard,et al.  Online reconfiguration considering variability demand: applications to real networks , 2004, IEEE Transactions on Power Systems.

[14]  Hoyong Kim,et al.  Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems , 1993 .

[15]  T. Taylor,et al.  Implementation of heuristic search strategies for distribution feeder reconfiguration , 1990 .

[16]  G. B. Jasmon,et al.  Network reconfiguration for load balancing in distribution networks , 1999 .

[17]  Taher Niknam,et al.  Multi-Objective Stochastic Distribution Feeder Reconfiguration in Systems With Wind Power Generators and Fuel Cells Using the Point Estimate Method , 2013, IEEE Transactions on Power Systems.

[18]  M. M. Adibi,et al.  Distribution Feeder Reconfiguration for Service Restoration and Load Balancing , 2000 .