Dahalan, W.M. and Mokhlis, Hazlie (2012) Network reconfiguration for loss reduction with distributed generations using PSO. In: 2012 IEEE International Conference on Power and Energy, PECon 2012, 2012, Kota Kinabalu.

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Abstract
This paper presents an effective method based on Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration and optimum value of DG size simultaneously. The main objective is to reduce the real power losses and improve the bus voltage profile in the system while satisfying all the distribution constraints. A method based on PSO algorithm to determine the minimum configuration is presented and their impact on the network real power losses and voltage profiles are investigated. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 33 bus systems and the results are presented and compare with the Genetic Algorithm (GA) method.
Item Type:  Conference or Workshop Item (Paper) 

Funders:  UNSPECIFIED 
Additional Information:  Conference code: 95813 Export Date: 17 April 2013 Source: Scopus Art. No.: 6450331 :doi 10.1109/PECon.2012.6450331 Language of Original Document: English Correspondence Address: Dahalan, W.M.; Department of Marine Electrical Engineering, Universiti Kuala Lumpur Malaysian Institute of Marine, Engineering Technology, Perak, Malaysia; email: wardiah@mimet.unikl.edu.my References: Nara, K., Shiose, A., Kitagawoa, M., Ishihara, T., Implementation of genetic algorithm for distribution systems loss minimum reconfiguration IEEE Transactions on Power Systems, 7 (3), pp. 10441051. , 1992 Aug; Civanlar, S., Grainger, J.J., Yin, H., Lee, S.S.H., Distribution feeder reconfiguration for loss reduction (1988) IEEE Trans. Power Del, 3 (3), pp. 12171223; Shirmohammadi, D., Hong, H.W., Reconfiguration of electric distribution networks for resistive line loss reduction (1989) IEEE Trans. Power Syst., 4 (1), pp. 14921498; Lopez, E., Opaso, H., Online reconfiguration considering the variability deman (2004) Applications to Real Networks, IEEE Trans. Power Syst, 19 (1), pp. 549553; Sawa, T., Radial network reconfiguration method in distribution system using mutation particle swarm optimization IEEE Burcharest Power Tech Conference, June 28th, Romania; Jin, X., Zhao, J., Sun, Y., Li, K., Zhang, B., Distribution network reconfiguration for load balancing using binary particle swarm optimization International Conference on Power System Technology, 1 (1), pp. 507510. , Nov. 2004; Zhou, Q., Shirmohammadi, D., Liu, E, W.H., Distribution feeder reconfiguration for operation cost reduction IEEE Transactions on Power Systems, 12 (2), pp. 730735. , May 1997; Wu, J.S., Tomsovic, K.L., Chen, C.S., A heuristic search approach to feeder switching operations for overload, faults, unbalanced flow and maintenance IEEE Transactions on Power Delivery, 6 (4), pp. 15791586. , Oct. 1991; Zhu, I.Z., Optimal reconfiguration of electrical distribution networks using the refined genetic algorithm (2002) Elect. Power Syst. Res, 62, pp. 3742; Huang, Y.C., Enhanced genetic algorithmbased fuzzy multi objective approach to distribution network reconfiguration (2002) Proc. Inst. Elect. Eng, 149 (5), pp. 615620; Su, C., Chang, C., Chiou, J., (2005) Distribution Network Reconfiguration for Loss Reduction by Ant Colony Search Algorithm Electric Power Systems Research, 75 (23), pp. 190199. , August; ChingTzong, S., Lee, C., Network reconfiguration of distribution systems using improved mixedinteger hybrid differential evolution (2003) IEEE Trans. Power Del, 18 (3), pp. 10221027; Rugthai Charoencheep, N., Sirisumrannukul, S., Feeder reconfiguration for loss reduction in distribution system with distributed generators by tabu search (2009) GMSARN International Journal, 3, pp. 4754; Wu, Y., Lee, C., Liu, L., Tsai, S., Study of reconfiguration for the distribution system with distributed generators IEEE Transactions on Power Delivery, 25 (3). , July 2010; Yasin, Z.M., Rahman, T.K.A., Network reconfiguration in a power distribution system under fault condition with the presence of distributed generation International Conference on Energy and Environment 2006 (ICEE 2006; Oliveria, M., Ochoa, L., Network reconfiguration and loss allocation for distribution systems with distributed generation (2004) IEEE/PES Trans. Distribution. Conference. Expos, pp. 206211; Sivanagaraju, S., Srikanth, Y., Jagadish Babu, E., An efficient genetic algorithm for loss minimum distribution system reconfiguration (2006) Electric Power Components and Systems, 34, pp. 249258. , 2006; Kim, H., Ko, Y., Artificial neural network based feeder reconfiguration for loss reduction in distribution systems (1993) IEEE Trans. Power Del, 8 (3), pp. 13561367; ElZonkoly, A.M., Optimal placement of multidistributed generation units including different load models using particle swarm optimization (2011) IET Generation Transmission & Distribution, 5 (7), pp. 760771. , July; Moradi, M.H., Abedini, M., A combination of genetic algorithm and particle swarm optimization for optimal dg location and sizing in distribution systems (2012) International Journal of Electrical Power & Energy Systems, 34 (1), pp. 6674. , Jan; Padma, L.M., Veera, C.R.V., Usha, V., Sivarami, R.N., Optimal dg placement for minimum real power loss in radial distribution system using pso (2010) ARPN Journal of Engineering and Applied Sciences, 5 (4), pp. 3037; Merlin, A., Back, H., Search for a minimalloss operating spanning tree configuration in an urban power distribution system (1975) Proceedings of the Fifth Power System Computation Conference, pp. 118. , Cambridge, UK Sponsors: IEEE Malaysia; IEEE Malaysia Power Electronics/Industrial; Electronics/Industrial Applications Joint Chapter; IEEE Malaysia Power and Energy Chapter 
Uncontrolled Keywords:  Loss Reduction and Distributed Generation; Particle Swarm Optimization; Reconfiguration; Bus systems; Bus voltage; Feeder reconfigurations; Loss reduction; Network reconfiguration; Optimum value, PSO algorithms; Real power loss; Switching operations; Voltage profile; Distributed power generation; Genetic algorithms; Particle swarm optimization (PSO); Computer simulation 
Subjects:  T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering 
Divisions:  Faculty of Engineering 
Depositing User:  Mr Jenal S 
Date Deposited:  10 Jul 2013 03:20 
Last Modified:  09 Oct 2019 08:55 
URI:  http://eprints.um.edu.my/id/eprint/7852 
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