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Vol.1 , No. 4, Publication Date: Sep. 22, 2014, Page: 199-205
 for solving optimal reactive power dispatch problem. This one is new framework relying on Quantum Computing principles and Cuckoo Search algorithm. The contribution consists in defining an appropriate representation scheme in the cuckoo search algorithm that allows applying successfully on combinatorial optimization problems some quantum computing principles like qubit representation, superposition of states, measurement, and interference. This hybridization between quantum inspired computing and bio inspired computing has led to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. In order to evaluate the efficiency of the proposed algorithm it has been tested in IEEE 57 bus system and compared to other algorithms .Simulation results show that QSCSA is more efficient in reducing the real power loss and also voltage deviations are minimized.&picture=http://www.aascit.org/aascit/decorators/img/journal/902.jpg)
| [1] | Kanagasabai Lenin, Jawaharlal Nehru Technological University Kukatpally, Hyderabad 500 085, India. |
| [2] | Bhumanapally Ravindhranath Reddy, Jawaharlal Nehru Technological University Kukatpally, Hyderabad 500 085, India. |
| [3] | Munagala Surya Kalavathi, Jawaharlal Nehru Technological University Kukatpally, Hyderabad 500 085, India. |
This paper presents a new Quantum Stirred Cuckoo Search Algorithm (QSCSA) for solving optimal reactive power dispatch problem. This one is new framework relying on Quantum Computing principles and Cuckoo Search algorithm. The contribution consists in defining an appropriate representation scheme in the cuckoo search algorithm that allows applying successfully on combinatorial optimization problems some quantum computing principles like qubit representation, superposition of states, measurement, and interference. This hybridization between quantum inspired computing and bio inspired computing has led to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. In order to evaluate the efficiency of the proposed algorithm it has been tested in IEEE 57 bus system and compared to other algorithms .Simulation results show that QSCSA is more efficient in reducing the real power loss and also voltage deviations are minimized.
Keywords
Optimal Reactive Power, Transmission Loss, Cuckoo Search Algorithm, Quantum Computing
Reference
| [01] | O.Alsac,and B. Scott, “Optimal load flow with steady state security”,IEEE Transaction. PAS -1973, pp. 745-751. |
| [02] | 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 |
| [03] | 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. |
| [04] | 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 |
| [05] | E. Hobson ,’Network consrained reactive power control using linear programming,‘ IEEE Transactions on power systems PAS -99 (4) ,pp 868=877, 1980 |
| [06] | 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. |
| [07] | 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. |
| [08] | 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 . |
| [09] | S.R.Paranjothi ,and K.Anburaja, “Optimal power flow using refined genetic algorithm”, Electr.Power Compon.Syst , Vol. 30, 1055-1063,2002. |
| [10] | D. Devaraj, and B. Yeganarayana, “Genetic algorithm based optimal power flow for security enhancement”, IEE proc-Generation.Transmission and. Distribution; 152, 6 November 2005. |
| [11] | 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. |
| [12] | 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. |
| [13] | 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. |
| [14] | 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. |
| [15] | 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. |
| [16] | 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. |
| [17] | 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. |
| [18] | 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. |
| [19] | 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. |
| [20] | 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. |
| [21] | Yang, X.-S., and Deb, S., Engineering Optimisation by Cuckoo Search, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 330–343, 2010. |
| [22] | Payne, R. B., Sorenson, M. D., and Klitz, K. The Cuckoos, Oxford University Press, 2005. |
| [23] | Barthelemy, P., Bertolotti, J., Wiersma, D. S., 2008. A Lévy flight for light. Nature, 453, 495-498. |
| [24] | Pavlyukevich, I. Lévy flights, non-local search and simulated annealing, J. Computational Physics, 226, 1830-1844, 2007. |
| [25] | Jaeger, G. Quantum Information: An Overview. Berlin: Springer. 2006 |
| [26] | Han, K.H. and Kim, J.H.. "Quantum-inspired Evolutionary Algorithms with a New Termination Criterion, Hε Gate, and Two Phase Scheme," IEEE Transactions on Evolutionary Computation, IEEE Press, vol. 8, no. 2, pp. 156-169, April 2004. |
| [27] | Layeb, A., Saidouni, D.E. A New Quantum Evolutionary Local Search Algorithm for MAX 3-SAT Problem. In Proceedings of the 3rd international Workshop on Hybrid Artificial intelligence Systems. Lecture Notes in Artificial Intelligence, vol. 5271. Springer-Verlag, Berlin, Heidelberg, (2008)172-179. |
| [28] | Draa A., Meshoul S., Talbi H., Batouche A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem. Int. Arab J. Inf. Technol. 7(1):pp. 21-27, 2010. |
| [29] | Layeb, A. Hybrid Quantum Scatter Search Algorithm for Combinatorial Optimization Problems. In the journal of Annals. Computer Science Series , ISSN: 1583-7165, Vol. 8, No.2, pp.227-244, 2010 |
| [30] | Layeb, A., Saidouni, D.E. A New Quantum Evolutionary Algorithm with Sifting Strategy for Binary Decision Diagram Ordering Problem, in the International Journal of Cognitive Informatics and Natural Intelligence. ISSN 1557-3958, Vol. 4. No 4, pp. 47-61, December 2010. |
| [31] | 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. |
| [32] | 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. |
