Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Reconfiguration of Electric Distribution Network Using Modified Particle Swarm Optimization

Print
PDF
International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 34 - Number 6
Year of Publication: 2011
Authors:
Anoop Arya
Yogendra Kumar
Manisha Dubey
10.5120/4131-5994

Anoop Arya, Yogendra Kumar and Manisha Dubey. Article: Reconfiguration of Electric Distribution Network Using Modified Particle Swarm Optimization. International Journal of Computer Applications 33(6):54-62, November 2011. Full text available. BibTeX

@article{key:article,
	author = {Anoop Arya and Yogendra Kumar and Manisha Dubey},
	title = {Article: Reconfiguration of Electric Distribution Network Using Modified Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {6},
	pages = {54-62},
	month = {November},
	note = {Full text available}
}

Abstract

This paper presents the application of modified form of Particle Swarm Optimization as an optimization technique to the reconfiguration of electric distribution systems. The intended reconfiguration is an optimization and decision-making process which considers the maximization of the number of loads supplied associated to the minimization of the number of closed switches. A novel selection regime for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO) without using external archives have been proposed. It means the algorithm is simple and computer coding is easy to implement to reconfiguration problem. The proposed methodology consists of use of the maximization function of the number of loads supplied and the loss minimization by the application of MOPSO. The developed algorithm has given the optimal solution in a reasonable computational time, compared to the dimension of the distribution system. Simulations for the test systems shows that the proposed MOPSO possesses better ability to finding the optimal Pareto front compared to the NSGA-II and classical PSO.

Reference

  • S. S. Venkata, A Pahwa, R. E. Brown and R. D. Christie, "What future distribution engineers need to learn", IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 1723, February 2004.
  • M. A. Kashem, V. Ganapathy and G. B. Jasmon, "Network reconfiguration for load balancing in distribution networks", lEEE Transactions on Generation, Transmission and Distribution, vol. 147, no. 3, pp. 171 - 175, May 2000.
  • Delfino B, Invernizzi M, Morini A, "Knowledge-based restoration guidelines." IEEE Transactions on Computer Applications in Power1992; 5:54–59.
  • Liu C-C, Liou K-L, Chu RF, Holen AT," Generation capability dispatch for bulk power system restoration: a knowledge-based approach". IEEE Transactions on Power Systems 1993; 8(1):316–325.
  • K.Aokietal., “Voltage drop constrained restoration of supply by switch operation in distribution systems,” IEEE Trans. on Power Delivery, vol. 3, pp.1267–1274, July 1988
  • H. J. Lee and Y. M. Park, “A restoration aid expert system for distribution substations,” IEEE Transactions on Power Del., vol. 11, no. 4, pp.1765–1769, Oct. 1996.
  • 7 Y. Fukuyama and H. D. Chiang, “A parallel genetic algorithm for service restoration in electric power distribution systems,” in Proc. IEEE FUZZ/IFES Conference, Yokohama, Japan, Mar. 1995.
  • Yogendra Kumar, Biswarup Das, Jaydev Sharma,” Genetic algorithm for supply restoration in distribution system with priority customers”,Int. Conference on Probabilistic Methods Applied to Power Systems KTH, Stockholm, Sweden, June 11-15, 2006-05-20
  • K. Nara et al., “Implementation of genetic algorithm for distribution Systems loss minimum re-configuration,” IEEE Transactions on Power System., vol.7, pp. 1044–1051, Aug. 1992.
  • S. Toune et al., “Comparative study of modern heuristic algorithms to service restoration in distribution systems,” IEEE Transactions on Power Del.,vol. 17, no. 1, pp. 173–181, Jan. 2002.
  • MarcosA.N.Guimaraes, JorgeF.C.Lorenzeti, CarlosA.Castro, “Reconfiguration of Distribution Systems for voltage stability margin enhancement using Tabu Search”, International Conference on Power System Technology POWERCON 2004, Singapore, pp.-1556-1561, 2004.
  • S.Toune, H. Fudo, T.Genji, Y.Fukuyama, “A reactive Tabu search for service restoration in electric power system”, Int. conf. on Evolutionary computaion, pp 763-768, May 1998.
  • H. Mori and H.Tani, “A hybrid method of PTS and ordinal optimization for distribution system service restoration”, IEEE Int. conf. on systems, Man and cybernatics, pp 3476-3483, Oct. 2003.
  • A.Augugliaro, L.Dusonchet and R.Sanseverino, “Service restoration in compensated distribution networks using a hybrid genetic algorithm”, Electric Power System Research, vol.46, no.1, pp59-66, July 1998.
  • H.C.Kuo and Y.Y.Hsu,“ Distribution system load estimation and service restoration using a fuzzy set approach,” IEEE Transactions on Power Del., vol. 8, no. 4, pp. 1950–1957, Oct. 1993.
  • Chao-Ming Huang,” Multiobjective Service Restoration of Distribution Systems Using Fuzzy Cause-Effect Networks”, IEEE Transactions on Power Systems, Vol. 18, No. 2, May 2003
  • Dariush S," Service restoration in distribution networks via network reconfiguration" IEEE Transactions on Power Delivery 1992; 7(2):952–958.
  • K.Deb, Amrit Pratap, Sameer Agrawal, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,IEEE Transactions on Evolutionary Computation.vol.6, no.2.April 2002.
  • Yogendra Kumar, Biswarup Das, Jaydev Sharma, “Multiobjective, multiconstraint service restoration of electrical power distribution system with priority customers” IEEE Transactions on Power Delivery, vol23, No.1, pp.261-270, 2008.
  • Augugliaro, Luigi Dusonchet ,"Evolving non-dominated solutions in multiobjective service restoration for automated distribution networks" Antonino 1, Eleonora Riva Sanseverino 25 July 2001
  • Huang Xianchao, G.A. Taylor,”Service Restoration of Distribution Systems Based on NSGA-II”, 2010.
  • Indira Mohanty, Jugal Kalita, Sanjoy Das, Anil Pahwa, Erik Buehler, “Ant Algorithm for the optimal restoration of Distribution feeders during cold load pickup’, IEEE Transactions on Power Delivery,pp.132-137, 2007.
  • Isamu Watanabe, “An ACO Algorithm for service Restoration in Power Distribution Systems”,IEEE Transactions on Power delivery, vol.4, no.3 pp.2864-2871, 2006.
  • 24Kennedy. J. and Eberhart. R, “Particle Swarm Optimization”, Proceedings of the Fourth IEEE Int.Conference on Neural Networks, Perth, Australia. 1995.
  • Reynolds, C.W.: Flocks, herds and schools: a distributed behavioural model, Computer Graphics, 21(4), p.25-34, 1987.
  • X. Hu, R.C. Eberhart, and Y. Shi, “ Particle Swarm with Extended Memory for Multi-objective Optimization” , In Proceedings of the IEEE Swarm Intelligence Symposium, pages 193–197,2003.
  • Rania Hassan, Babak Cohanim,Olivier de Weck,“ A comparison of particle swarm optimisation and Genetic Algorithm” , Journal of American Institute of Aeronautics and Astronautics (AAIA),2005
  • J. Kennedy and R.C. Eberhart , “ A Discrete Binary Version of the Particle Swarm Algorithm” In Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, pp 4104– 4109, 1997.
  • Wu-Chang Wu, Men-Shen Tsai and Fu-Yuan Hsu, “A New Binary Coding Particle Swarm Optimization for Feeder Reconfiguration” IEEE Transactions on Power Systems, vol 2.no. 4 pp 243-249, 2009
  • Lambert-Torres, G., Martins, H. G., Coutinho, M. P., Salomon, C. P.,Filgueiras, L. S., “Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems”, in Proc. of The 3rd International Symposium on Intelligence Computational Applications, ISICA2008, Wuhan,China, 2008.
  • A. Y. Abdelaziz,S. F. Mekhamer,F. M. Mohammed,M. A. L. Badr, “A Modified Particle Swarm Technique for Distribution Systems Reconfiguration” Online Journal on Electronics and Electrical Engineering (OJEEE) Vol. (1) – No. (2), 2010
  • C.A. CoelloCoello and M.S. Lechuga. “MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization” in Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages 1051–1056, 2002.
  • C.A. CoelloCoello, G. Toscano Pulido, and M. Salazar Lechuga, “Handling Multiple Objectives with Particle Swarm Optimization” in IEEE Transactions on Evolutionary Computation, vol.8, no.3 2004.