GREEN DARNER ALGORITHM FOR SOLVING OPTIMAL POWER FLOW PROBLEM
Keywords:Green Darner, Optimization, Optimal Reactive Power, Transmission Loss
In this paper a Green Darner (GD) algorithm is used to solve optimal reactive power problem. The key inducement of the Green Darner (GD) algorithm initiated from inert and vibrant swarming behaviors. These two swarming behaviours are very similar to the two key segment of optimization using meta-heuristics: exploration and exploitation. Green Darner (GD) engenders sub swarms and flies over assorted areas in an inert swarm, & it will be the main goal of the exploration segment. In the inert swarm, conversely, Green Darner (GD) flies in bigger swarms and down to one direction, which is constructive in the exploitation segment. In this proposed Green Darner (GD) algorithm, two necessary segments of optimization, exploration and exploitation, are planned by modeling the social communication of Green Darner (GD) in navigating, probing for foods, and keep away from enemies when swarming vigorously or statistically. The projected Green Darner (GD) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the improved performance of the projected Green Darner (GD) algorithm in reducing the real power loss.
O.Alsac,and B. Scott, “Optimal load flow with steady state security”, IEEE Transaction. PAS -1973, pp. 745-751. DOI: https://doi.org/10.1109/TPAS.1974.293972
Lee K Y, Paru Y M, Oritz J L –A united approach to optimal real and reactive power dispatch , IEEE Transactions on power Apparatus and systems 1985: PAS-104 : 1147-1153 DOI: https://doi.org/10.1109/TPAS.1985.323466
A.Monticelli , M .V.F Pereira ,and S. Granville , “Security constrained optimal power flow with post contingency corrective rescheduling” , IEEE Transactions on Power Systems :PWRS-2, No. 1, pp.175-182.,1987. DOI: https://doi.org/10.1109/TPWRS.1987.4335095
DeebN ,Shahidehpur S.M ,Linear reactive power optimization in a large power network using the decomposition approach. IEEE Transactions on power system 1990: 5(2) : 428-435 DOI: https://doi.org/10.1109/59.54549
E. Hobson ,’Network consrained reactive power control using linear programming, ‘ IEEE Transactions on power systems PAS -99 (4) ,pp 868=877, 1980 DOI: https://doi.org/10.1109/TPAS.1980.319715
K.Y Lee, Y.M Park, and J.L Oritz, “Fuel –cost optimization for both real and reactive power dispatches” , IEE Proc; 131C,(3), pp.85-93.1992. DOI: https://doi.org/10.1049/ip-c.1984.0012
M.K. Mangoli, and K.Y. Lee, “Optimal real and reactive power control using linear programming” , Electr.PowerSyst.Res, Vol.26, pp.1-10,1993. DOI: https://doi.org/10.1016/0378-7796(93)90063-K
C.A. Canizares , A.C.Z.de Souza and V.H. Quintana , “ Comparison of performance indices for detection of proximity to voltage collapse ,’’ vol. 11. no.3, pp.1441-1450, Aug 1996.
K.Anburaja, “Optimal power flow using refined genetic algorithm”, Electr.PowerCompon.Syst, Vol. 30, 1055-1063, 2002. DOI: https://doi.org/10.1080/15325000290085343
D. Devaraj, and B. Yeganarayana, “Genetic algorithm based optimal power flow for security enhancement”, IEE proc-Generation.Transmission and. Distribution; 152, 6 November 2005. DOI: https://doi.org/10.1049/ip-gtd:20045234
A. Berizzi, C. Bovo, M. Merlo, and M. Delfanti, “A ga approach to compare orpf objective functions including secondary voltage regulation,” Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194, 2012. DOI: https://doi.org/10.1016/j.epsr.2011.11.014
C.-F. Yang, G. G. Lai, C.-H.Lee, C.-T. Su, and G. W. Chang, “Optimal setting of reactive compensation devices with an improved voltage stability index for voltage stability enhancement,” International Journal of Electrical Power and Energy Systems, vol. 37, no. 1, pp. 50 – 57, 2012. DOI: https://doi.org/10.1016/j.ijepes.2011.12.003
P. Roy, S. Ghoshal, and S. Thakur, “Optimal var control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization,” International Journal of Electrical Power and Energy Systems, vol. 43, no. 1, pp. 830 – 838, 2012. DOI: https://doi.org/10.1016/j.ijepes.2012.05.032
B. Venkatesh, G. Sadasivam, and M. Khan, “A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy lp technique,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 844 – 851, may 2000. DOI: https://doi.org/10.1109/59.867183
W. Yan, S. Lu, and D. Yu, “A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 913 – 918, may 2004. DOI: https://doi.org/10.1109/TPWRS.2004.826716
W. Yan, F. Liu, C. Chung, and K. Wong, “A hybrid genetic algorithminterior point method for optimal reactive power flow,” IEEE Transactions on Power Systems, vol. 21, no. 3, pp. 1163 –1169, aug. 2006.
J. Yu, W. Yan, W. Li, C. Chung, and K. Wong, “An unfixed piecewiseoptimal reactive power-flow model and its algorithm for ac-dc systems,” IEEE Transactions on Power Systems, vol. 23, no. 1, pp. 170 –176, feb. 2008. DOI: https://doi.org/10.1109/TPWRS.2007.907387
F. Capitanescu, “Assessing reactive power reserves with respect to operating constraints and voltage stability,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2224–2234, nov. 2011.
Z. Hu, X. Wang, and G. Taylor, “Stochastic optimal reactive power dispatch: Formulation and solution method,” International Journal of Electrical Power and Energy Systems, vol. 32, no. 6, pp. 615 – 621, 2010. DOI: https://doi.org/10.1016/j.ijepes.2009.11.018
A. Kargarian, M. Raoofat, and M. Mohammadi, “Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads,” Electric Power Systems Research, vol. 82, no. 1, pp. 68 – 80, 2012. DOI: https://doi.org/10.1016/j.epsr.2011.08.019
Thorp JH, Rogers DC (2014) Thorp and Covich’s freshwater invertebrates: ecology and general biology. Elsevier, Amsterdam
Wikelski M, Moskowitz D, Adelman JS, Cochran J, Wilcove DS, May ML (2006) Simple rules guide dragonfly migration. Biol Lett 2:325–329 DOI: https://doi.org/10.1098/rsbl.2006.0487
Russell RW, May ML, Soltesz KL, Fitzpatrick JW (1998) Massive swarm migrations of dragonflies (Odonata) in eastern North America. AmMidl Nat 140:325–342. DOI: https://doi.org/10.1038/26348
S. Mirjalili, "Dragonfly Algorithm: A New Meta-heuristic Optimization Technique for Solving Single-objective, Discrete, and Multi-objective Problems", Neural Computing and Applications, in press, 2015. DOI: https://doi.org/10.1007/s00521-015-1920-1
Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Gr 21:25–34. DOI: https://doi.org/10.1145/37402.37406
HüseyinHakli and Harun Uğuz, “Levy Flight Distribution for Scout Bee in Artificial Bee Colony Algorithm” Lecture Notes on Software Engineering, Vol. 1, No. 3, August 2013. DOI: https://doi.org/10.7763/LNSE.2013.V1.55
IEEE, “The IEEE 30-bus test system and the IEEE 118-test system”, (1993), http://www.ee.washington.edu/trsearch/pstca/.
Jiangtao Cao, Fuli Wang and Ping Li, “An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow”, International Journal of Control and Automation Vol.7, No.3 (2014), pp.161-176
How to Cite
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.