REDUCTION OF REAL POWER LOSS BY IMPROVED QUANTUM ALGORITHM
DOI:
https://doi.org/10.29121/granthaalayah.v5.i6.2017.2095Keywords:
Quantum Particle Swarm Optimization, Genetic Particle Swarm Optimization, Reactive Power, Transmission LossAbstract [English]
In this paper, combination of the Q-bit evolutionary search - quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search - genetic particle swarm optimization (GPSO) has been done to solve the reactive power problem & termed as Improved Quantum Algorithm (IQA). Proposed IQA can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which augments the penetrating behavior to augment and balance the exploration and exploitation aptitudes in the whole searching space. In order to evaluate the performance of the proposed IQA algorithm, it has been tested on IEEE 57,118 bus systems and compared to other standard algorithms.
Downloads
References
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
Deeb N ,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. 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.Power Syst.Res, Vol.26, pp.1-10,1993. DOI: https://doi.org/10.1016/0378-7796(93)90063-K
S.R.Paranjothi ,and K.Anburaja, “Optimal power flow using refined genetic algorithm”, Electr.Power Compon.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
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 .
Wu Q H, Ma J T. Power system optimal reactive power dispatch using evolutionary programming. IEEE Transactions on power systems 1995; 10(3): 1243-1248 . DOI: https://doi.org/10.1109/59.466531
S.Durairaj, D.Devaraj, P.S.Kannan ,’ Genetic algorithm applications to optimal reactive power dispatch with voltage stability enhancement’ , IE(I) Journal-EL Vol 87,September 2006.
D.Devaraj ,’ Improved genetic algorithm for multi – objective reactive power dispatch problem’ European Transactions on electrical power 2007 ; 17: 569-581. DOI: https://doi.org/10.1002/etep.146
P. Aruna Jeyanthy and Dr. D. Devaraj “Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm” International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August, 2010 1793-8163. DOI: https://doi.org/10.7763/IJCEE.2010.V2.220
Kennedy, J. & Eberhart, R.C. (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks: 1942-1948. NJ: Piscataway.
Van den Bergh, F. & Engelbrecht, A.P. (2000) Cooperative learning in neural network using particle swarm optimizers. South African Computer Journal 26: 84-90,.
El-Galland, AI., El-Hawary, ME. & Sallam, AA. (2001) Swarming of intelligent particles for solving the nonlinear constrained optimization problem. Engineering Intelligent Systems for Electrical Engineering and Communications 9: 155-163.
Parsopoulos, K.E. & Vrahatis, M.N. (2002) Recent approaches to global optimization problems through Particle Swarm Optimization. Natural Computing 1(2-3): 235-306. DOI: https://doi.org/10.1023/A:1016568309421
Kennedy, J. & Eberhart, R.C. (1997) A discrete binary version of the particle swarm algorithm. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics: 4104-4109. NJ: Piscatawary.
Franken, N. & Engelbrecht, A.P. (2005) Investigating binary PSO parameter influence on the knights cover problem. IEEE Congress on Evolutionary Computation 1: 282-289. DOI: https://doi.org/10.1109/CEC.2005.1554696
Huang, Y.-X., Zhou, C.-G., Zou, S.-X. & Wang, Y. (2005) A hybrid algorithm on class cover problems. Journal of Software (in Chinese) 16(4): 513-522. DOI: https://doi.org/10.1360/jos160513
Yang, S.Y., Wang, M. & Jiao, L.C. (2004) A quantum particle swarm optimization. Proceeding of the 2004 IEEE Congress on Evolutionary Computation 1: 320-324,.
Yin, P.Y. (2006) Genetic particle swarm optimization for polygonal approximation of digital curves. Pattern Recognition and Image Analysis 16(2): 223-233,. DOI: https://doi.org/10.1134/S105466180602009X
Han, K.H. & Kim, J.H. (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Transactions on Evolutionary Computation 6(6): 580-593. DOI: https://doi.org/10.1109/TEVC.2002.804320
Wolpert, D.H. & Macready, W.G. (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1): 67–82. DOI: https://doi.org/10.1109/4235.585893
Wang, L. & Zheng, D.Z. (2001) An effective hybrid optimization strategy for job-shop scheduling problems. Computers and Operations Research 28: 585–596. DOI: https://doi.org/10.1016/S0305-0548(99)00137-9
Wang, L., Tang, F. & Wu, H. (2005) Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Applied Mathematics and Computation 171: 1141–1156. DOI: https://doi.org/10.1016/j.amc.2005.01.115
Back, T. (1994) Selective pressure in evolutionary algorithm: a characterization of selection mechanisms. Proceeding of the first IEEE Conference on Evolutionary Computation, IEEE Press: 57-62. Piscataway, HJ.
Ahmadjian, V. & Paracer, S. (2000) Symbiosis: An Introduction to Biological Associations. Oxford University Press, New York.
Douglas, A.E. (1994) Symbiotic Interactions. Oxford University Press, Oxford.
Chaohua Dai, Weirong Chen, Yunfang Zhu, and Xuexia Zhang, “Seeker optimization algorithm for optimal reactive power dispatch,” IEEE Trans. Power Systems, Vol. 24, No. 3, August 2009, pp. 1218-1231. DOI: https://doi.org/10.1109/TPWRS.2009.2021226
J. R. Gomes and 0. R. Saavedra, “Optimal reactive power dispatch using evolutionary computation: Extended algorithms,” IEE Proc.-Gener. Transm. Distrib.. Vol. 146, No. 6. Nov. 1999. DOI: https://doi.org/10.1049/ip-gtd:19990683
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.
Downloads
Published
How to Cite
Issue
Section
License
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.