Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm

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Manju Mam 1,* Leena G 1 N.S. Saxena 2

1. EEE Dept, Faculty of Engineering and Technology, Manav Rachna International University, Faridabad, India

2. Operations Faculty Member, Gurgoan, Haryana, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2016.02.03

Received: 12 Nov. 2015 / Revised: 25 Dec. 2015 / Accepted: 20 Jan. 2016 / Published: 8 Mar. 2016

Index Terms

Feeder reconfiguration, Bacterial foraging optimization, Backward-Forward sweep, Power loss reduction, Radial network, Distribution system, Geographical information system


This paper presents a distribution network reconfiguration based on bacterial foraging optimization algorithm (BFOA) along with backward-forward sweep (BFS) load flow method and geographical information system (GIS). Distribution network reconfiguration (DNR) is a complex, non-linear, combinatorial, and non-differentiable constrained optimization process aimed at finding the radial structure that minimized network power loss while satisfying all operating constraints. BFOA is used to obtain the optimal switching configuration which results in a minimum loss, BFS is used to optimize the deviation in node voltages, and GIS is used for planning and easy analysis purposes. Simulation is performed on the 33-bus system and results are compared with the other approaches. The obtained results show that the proposed approach is better in terms of efficiency and having good convergence criteria.

Cite This Paper

Manju Mam, Leena G, N.S. Saxena,"Distribution Network Reconfiguration for Power Loss Minimization Using Bacterial Foraging Optimization Algorithm", International Journal of Engineering and Manufacturing(IJEM), Vol.6, No.2, pp.18-32, 2016. DOI: 10.5815/ijem.2016.02.03


[1]Augugliaro A, Dusonchet L, Ippolito M, Sanseverino E R. Minimum losses reconfiguration of MV distribution networks through local control of tie-switches. IEEE Trans. Power Del. 2003:18(3): 762–771.

[2]Kim H, Ko Y. Artificial neural network based feeder reconfiguration for loss reduction in distribution systems. IEEE Trans. Power Del. 1993:8(3): 1356–1367.

[3]Chiou J P, Wang F S. Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to fed-batch fermentation process. Comput. Chem. Eng. 1999:23: 1277–1291.

[4]Chiou J, Chang C. Variable scaling hybrid differential evaluation for solving network reconfiguration of distribution system. IEEE Trans. Power Syst. 2005:20(2): 668–674.

[5]Delbem A, Carvalho A, Bretas N. Main chain representation for evolutionary algorithms applied to distribution system reconfiguration. IEEE Trans. Power Syst. 2005:20(1): 425–436.

[6]Gomes V, Carneiro S. A new reconfiguration algorithm for large distribution systems. IEEE Trans. Power Del. 2005:20(3): 1373–1378.

[7]Taher N, Reza K, Bahman B F. A hybrid evolutionary algorithm for distribution feeder reconfiguration. Sadhana 2010:35(2):139-162.

[8]Mostafa S, Marzieh D, Mohammad S, Hadi H K. Optimal reconfiguration and capacitor placement for power loss reduction of distribution system using improved binary particle swarm optimization. Int. J. Energy Environ Eng. 2014:5:73.

[9]W.M. Ritchie, P.W. Beard, A. Barker, D.G.T. Lewis. Loss Reduction – an overview of the problems and solutions. Power Technol Int. 1988:191–194.

[10]Civanlar S., Grainger J.J., Yin H., Lee S.S.H. Distribution feeder reconfiguration for loss reduction. IEEE Trans. on Power Del. 1988:3(3):1217-1223.

[11]Dolatdar E, Soleymani S, Mozafari B. A new distribution network reconfiguration approach using a tree model. World Academy of Science, Engineering and Technology 2009:58:1186-1193.

[12]Boor Z, Hosseini S M. GA based optimal placement of DGs for loss reduction and reliability improvement in distribution networks with time varying loads. I.J. Intelligent Systems and Applications 2013:04:55-63.

[13]Merlin A, Back H. Search for a minimal-loss operating spanning tree configuration in an urban power distribution system. In Proc. 5th Power System computation Conf. (PSCC), Cambridge, U.K 1975:1-18.

[14]Shirmohammadi D, Hong H W. Reconfiguration of electric distribution networks for resistive line losses reduction. IEEE Trans. on Power Del. 1989:4(2):1492-1498.

[15]Nara K, Shiose A, Kitagawoa M, Ishihara T. Implementation of genetic algorithm for distribution systems loss minimum reconfiguration. IEEE Trans. Power Syst. 1992:7(3):1044-1051.

[16]Zhu J Z. Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Elect. Power Syst. Res. 2002:62:37-42.

[17]Chiang H D, Rene J J. Optimal network reconfiguration in distribution systems: Part 1: A new formulation and a solution methodology. IEEE Trans. Power Del. 1990:5(4):1902-1908.

[18]Cheng H C, Kou C C. Network reconfiguration in distribution systems using simulated annealing. Elect. Power Syst. Res. 1994:29:227-238.

[19]Das D. A fuzzy multiobjective approach for network reconfiguration of distribution systems. IEEE Trans. Power Del. 2006:21(1):202-209.

[20]Kumar K S, Jayabarathi T. Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm. Electrical Power and Energy Systems 2012:36:13-17.

[21]Afzalan M, Taghikhani M A. Placement and sizing of DG using PSO&HBMO algorithm in radial distribution network. I.J. Intelligent Systems and Applications. 2012:10:43-49.

[22]Ehsan M, Javad R. A new method for load flow on radial distribution network. In 12th Iranian Student Conference on Electrical Engineering (ISCEE) 2009.

[23]Passino K M. Biomimicry of bacterial foraging for distribution optimization and control. IEEE Control Syst. Mag. 2002:22(3):52-67.

[24]Liu Y, Passino K M. Biomimicry of social foraging bacteria for distribution optimization: Models, Principles, and emergent behaviors. J. Optimization Theory Appl. 2002:115(3):603-628.

[25]Tripathy M, Mishra S, Lai L L, Zhang Q P. Transmission loss reduction based on FACTS and bacteria foraging algorithm. In Proc. PPSN. 2006:222-231.

[26]Kim D H, Cho C H. Bacterial foraging based neural network fuzzy learning. In Proc. IICAI 2005:2030-2036.

[27]Mishra S, Bhende C N. Bacterial foraging technique-based optimized active power filter for load compensation. IEEE Trans. Power Del. 2007:22(1):457-465.

[28]Mishra S. A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans. Evol. Comput. 2005:9(1):61-73.

[29]Gopi E S. Mathematical summary for digital signal processing applications with matlab. Springer; 2010.

[30]Rao R S, Narasimham S V L, Raju M R, Rao A S. Optimal network reconfiguration of large scale distribution system using harmony search algorithm. IEEE Trans. On Power Syst. 2011:26(3):1080-1088.