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The Simultaneous Application of Optimum Network Reconfiguration and Distributed Generation Sizing Using PSO for Power Loss Reduction

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Warianty tytułu
PL
Optymalizacja rekonfiguracji sieci elektroenergetycznej i rozmiarów generatorów rozproszonych w redukcji strat energetycznych – zastosowanie algorytmu PSO
Języki publikacji
EN
Abstrakty
EN
The utilization of Distributed Generation (DG) sources in Distribution Power system is indeed vital as it is capable of solving problems especially pertaining to power losses due to an increasing demand for electrical energy.The location and optimal size of DG has become a prominent issue for the network to have lower power losses value. In order to reduce unnecessary power losses, the use of a combination reconfiguration method and DG units can assist the system to obtain optimal power loss in the network distribution. The primary idea is to have the reconfiguration process embedded with Distributed Generation (DG) and being operated simultaneously to reduce power losses and determine the optimal size of DG by using Particle Swarm Optimization (PSO). The objective of this paper is to focus on reducing the real power losses in the system as well as improving the voltage profile while fulfilling distribution constraints. The simulation results show that the use of simultaneous approach has resulted the lower power losses and better voltage profile of the system. A detail performance analysis is carried out on IEEE 33-bus systems demonstrate the effectiveness of the proposed methodology.
PL
W artykule przedstawiono metodę przeprowadzenia rekonfiguracji systemie elektroenergetycznym z wykorzystaniem generatorów rozproszonych. Do zadań głównych należy ograniczenie strat i optymalizacja rozmiarów generatorów, przy jednoczesnym zapewnieniu stabilności systemu. W rozwiązaniu wykorzystano metodę PSO. Przedstawiono wyniki badań symulacyjnych oraz analizę szczegółową dla systemu IEEE 33- liniowego.
Rocznik
Strony
137--141
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur
  • Department of Marine Elec. Engineering, University Kuala Lumpur, (Malaysian Institute of Marine Engineering Technology), Perak
  • UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Kuala Lumpur, Malaysia
autor
  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur
  • UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Kuala Lumpur, Malaysia
autor
  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur
  • UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Kuala Lumpur, Malaysia
autor
  • Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur
  • Faculty of Electrical Engineering, Universiti Teknologi Malaysia
Bibliografia
  • [1] J. Mendoza, R. Lopez, D. Morales, E. López, R. Moraga, and P. Dessante, “Minimal Loss Reconfiguration using Genetic Algorithm with Restricted Population and Addressed Operators:Real Application”, IEEE Transactions on Power Systems, vol. 21, no.2, May. 2006, pp. 948-954.
  • [2] D. Shirmohammadi, H.W. Hong, “Reconfiguration of electric distribution networks for resistive line loss reduction”, IEEE Trans.Power Syst. 4 (1) (1989) 1492–1498.
  • [3] J.Y.Fang ZongWang, “A Refined Plant Growth Simulation Algorithm for Distribution Network Reconfiguration”, IEEE Trans.on Power Systems, 2009, pp.4244-4738.
  • [4] K. Sathish Kumar, T. Jayabarathi, “Power system reconfiguration and loss minimization for distribution systems using bacterial foraging optimization algorithm,” Electrical Power and Energy Systems 36, Nov 2011, pp. 13–17.
  • [5] I.Z. Zhu, “Optimal reconfiguration of electrical distribution networks using the refined genetic algorithm”, Elect. Power Syst. Res. 62(2002) 37–42.
  • [6] Y.C.Huang, “Enhanced genetic algorithm-based fuzzy multi objective approach to distribution network reconfiguration”, Proc. Inst .Elect. Eng. 149 (5) (2002) 615–620.
  • [7] H. Kim, Y. Ko, “Artificial neural network based feeder reconfiguration for loss reduction in distribution systems”, IEEE Trans. Power Del. 8 (3) (1993) 1356–1367.
  • [8] M.Padma Lalitha, V.C Veera Reddy, V. Usha, “Optimal DG Placement for Maximum Loss Reduction in Radial Distribution System using ABC algorithm”. Journal of Theoretical and Applied Information Technology, 2005-2010
  • [9] M.Padma Lalitha, V.C Veera Reddy, V. Usha, “Optimal DG Placement for Minimum Real Power Loss in Radial Distribution Systems Using PSO”. Journal of Theoretical and Applied Information Technology, 2005-2010.
  • [10] E. Lopez, h. Opaso., “Online reconfiguration considering variability deman”, applications to real networks, IEEE Trans. Power Syst.19 (1) (Singh, Devender Singh, and K.S. Verma, “GA based optimal sizing and placement of distributed generation for loss minimization”, International Journal of Electrical and Computer Engineering 2:8 2007, pp 556-562 (ONLINE), ISSN : 1307-5179.
  • [11] M. H. Moradi and M. Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems, International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, Jan. 2012, pp. 66-74.
  • [12] Nerves, A.C.; Roncesvalles, J.C.K.; , Application of evolutionary programming to optimal siting and sizing and optimal scheduling of distributed generation, TENCON 2009 - 2009 IEEE Region 10 Conference , vol., no., pp.1-6, 23-26 Jan. 2009.
  • [13] Yasin, Z.M.; Rahman, T.K.A.; Musirin, I.; Rahim, S.R.A., Optimal sizing of distributed generation by using quantuminspired evolutionary programming, Power Engineering and Optimization Conference (PEOCO), 2010 4th International , vol., no., 23-24 June 2010, pp.468-473.
  • [14] Ching-Tzong Su, Chung-Fu Chang and Ji-Pyng Chiou, “Distribution Network Reconfiguration for Loss Reduction by Ant Colony Search Algorithm”, Electric Power Systems Research, Vol. 75, No. 2-3, August 2005, pp. 190–199.
  • [15] 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 (ICEE), 28-30 Aug.2006.
  • [16] Z Zhu, “Optimal reconfiguration of electrical distribution networks using the refined GA” Electric Power System Research, Vol. 62, 2002, pp. 37-84.
  • [17] J.Olamie, T.Niknam, G. Gharehpetian, “Application of Particle Swarm Optimization for Distribution feeder Reconfiguration Considering Distributed Generators”, Applied Mathematics and Computation, 2008, pp. 575-586.
  • [18] Y. K. Wu, C. Y. Lee, L. C. Liu and S. H. Tsai, Study of Reconfiguration for the Distribution System With Distributed Generators, IEEE Trans. on Power Del. 25, No. 3, 1678-1685.
  • [19] N. Rugithaicharoencheep; S. Sirisumarannukul, Feeder reconfiguration for loss reduction in distribution system with Journal, vol 3, 2009, pp. 47-54.
  • [20] K. Prasad, R. Ranjan, Optimal reconfiguration of radial distribution system using a fuzzy mutated genetic algorithm, IEEE Trans. Power Del. 20 (2) (2005) 1211–1213.
  • [21] J.J. Jamian, M.W. Mustafa, H. Mokhlis, M.N. Abdullah, Comparative Study on Distributed Generator Sizing Using Three Types of Particle Swarm Optimization, Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference no., 8-10 Feb. 2012, 131-136
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9ec3657a-3552-46ab-a421-998112e9162f
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