• Dr.K.Lenin Professor, Prasad V. Potluri Siddhartha Institute of Technology Kanuru, Vijayawada, Andhra Pradesh -520007, India



Biological Particle Swarm, Reactive Power Optimization, Transmission Loss

Abstract [English]

This paper presents Advanced Particle Swarm Optimization (APSO) algorithm for solving optimal reactive power problem. In this work Biological Particle swarm Optimization algorithm utilized to solve the problem by eliminating inferior population & keeping superior population, to make full use of population resources and speed up the algorithm convergence. Projected Advanced Particle Swarm Optimization (APSO) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Advanced Particle Swarm Optimization (APSO) algorithm in reducing the real power loss and static voltage stability margin (SVSM) Index has been enhanced.


Download data is not yet available.


O.Alsac, B. Scott, “Optimal load flow with steady state security”, IEEE Transaction. PAS -1973, pp. 745-751. DOI:

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:

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:

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:

E. Hobson,’Network consrained reactive power control using linear programming, ‘IEEE Transactions on power systems PAS -99 (4), pp 868=877, 1980 DOI:

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:

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:

C.A. Canizares, 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.Power Compon.Syst , Vol. 30, 1055-1063,2002. DOI:

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:

A. Berizzi, C. Bovo, M. Merlo, and M. Delfanti, “A ga approach tocompare orpf objective functions including secondary voltage regulation,”Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194,2012. DOI:

C.-F. Yang, G. G. Lai, C.-H.Lee, C.-T. Su, and G. W. Chang, “Optimalsetting of reactive compensation devices with an improved voltagestability index for voltage stability enhancement,” International Journalof Electrical Power and Energy Systems, vol. 37, no. 1, pp. 50 – 57,2012. DOI:

P. Roy, S. Ghoshal, and S. Thakur, “Optimal var control for improvementsin voltage profiles and for real power loss minimization usingbiogeography based optimization,” International Journal of ElectricalPower and Energy Systems, vol. 43, no. 1, pp. 830 – 838, 2012. DOI:

B. Venkatesh, G. Sadasivam, and M. Khan, “A new optimal reactivepower scheduling method for loss minimization and voltage stabilitymargin maximization using successive multi-objective fuzzy lp technique,”IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 844 –851, may 2000. DOI:

W. Yan, S. Lu, and D. Yu, “A novel optimal reactive power dispatchmethod based on an improved hybrid evolutionary programming technique,”IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 913 –918, may 2004. DOI:

W. Yan, F. Liu, C. Chung, and K. Wong, “A hybrid genetic algorithminteriorpoint method for optimal reactive power flow,” IEEE Transactionson Power Systems, vol. 21, no. 3, pp. 1163 –1169, aug. 2006.

J. Yu, W. Yan, W. Li, C. Chung, and K. Wong, “An unfixed piecewiseoptimalreactive 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:

F. Capitanescu, “Assessing reactive power reserves with respect tooperating constraints and voltage stability,” IEEE Transactions on PowerSystems, vol. 26, no. 4, pp. 2224–2234, nov. 2011.

Z. Hu, X. Wang, and G. Taylor, “Stochastic optimal reactive powerdispatch: Formulation and solution method,” International Journal ofElectrical Power and Energy Systems, vol. 32, no. 6, pp. 615 – 621,2010. DOI:

A. Kargarian, M. Raoofat, and M. Mohammadi, “Probabilistic reactivepower procurement in hybrid electricity markets with uncertain loads,”Electric Power Systems Research, vol. 82, no. 1, pp. 68 – 80, 2012. DOI:

R C Eberhart and J Kennedy, “A New Optimizer Using Particles Swarm Theory,” In: Proc Sixth InternationalSymposium on Micro Machine and Human Science, Nagoya, Japan, 1995.

Eberhart R C and Shi Y, “Particle swarm optimization: developments, applications and resources,” Proc. Congress on Evolutionary Computation 2001. Piscataway, NJ: IEEE Press, pp. 81–86, 2001.

Parsopouls K.E., Plagianakos V.P., Magoulas G.D., and Vrahatis M.N., “Stretching technique for obtaining global minimizers through particle swarm optimization,” In: Proc Workshop on Particle Swarm Optimization, Indianapolis USA, pp. 22–29, 2001.

Silva A., Neves A., and Costa E., “SAPPO: A simple, adaptable, predator prey optimizer,” Lecture Notes in Artificial Intelligence, vol. 2909, pp.59–73, 2003.

He S., Wu Q.H., Wen J.Y., Saunders J.R., and Paton R.C., “A particle swarm optimizer with passive congregation,” BioSystems, vol. 78, pp. 135–147, 2004. DOI:

Niu B., Zhu Y.L., and He X.X., “Construction of fuzzy models for dynamical systems using multiple cooperative particle swarm optimizer,” Lecture Notes in Artificial Intelligence, vol. 3613, pp. 987–1000, 2005.

Hendtlass T., “Preserving diversity in particle swarm optimization,” Lecture Notes in Artificial Intelligence, vol. 2718, pp. 31–40, 2003.

Li Qinghua, Yang Shida, and Yuan Youlin, “Improving Optimization for Genetic Algorithms Based on Level Set,” Journal of Computer Research and Development, vol. 43, pp. 1624–1629, 2006.

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:

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:

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:




How to Cite


Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>