CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM

Authors

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

DOI:

https://doi.org/10.29121/granthaalayah.v6.i6.2018.1369

Keywords:

Optimal Reactive Power, Transmission Loss, Crowding Distance Based Particle Swarm Optimization

Abstract [English]

In this paper, Crowding Distance based Particle Swarm Optimization (CDPSO) algorithm has been proposed to solve the optimal reactive power dispatch problem. Particle Swarm Optimization (PSO) is swarm intelligence-based exploration and optimization algorithm which is used to solve global optimization problems. In PSO, the population is referred as a swarm and the individuals are called particles. Like other evolutionary algorithms, PSO performs searches using a population of individuals that are updated from iteration to iteration. The crowding distance is introduced as the index to judge the distance between the particle and the adjacent particle, and it reflects the congestion degree of no dominated solutions. In the population, the larger the crowding distance, the sparser and more uniform. In the feasible solution space, we uniformly and randomly initialize the particle swarms and select the no dominated solution particles consisting of the elite set. After that by the methods of congestion degree choosing (the congestion degree can make the particles distribution more sparse) and the dynamic e infeasibility dominating the constraints, we remove the no dominated particles in the elite set. Then, the objectives can be approximated. Proposed crowding distance based Particle Swarm Optimization (CDPSO) algorithm has been tested in standard IEEE 30 bus test system and simulation results shows clearly the improved performance of the projected algorithm in reducing the real power loss and static voltage stability margin has been enhanced.

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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

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. 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

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.

Berizzi.C.Bovo,M.Merlo, and M.Delfanti,(2012), “A GA approach to compare ORPF objective functions including secondary voltage regulation,” Electric Power Systems Research, vol. 84, no. 1, pp. 187 – 194.

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 improvement sin 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 algorithm interior 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 piecewise optimal 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

Kennedy J, EberhartR. Particle swarm optimization. Proceedings of the IEEE international conference on neural networks (Perth, Australia), 1942–1948. Piscataway, NJ: IEEE Service Center; 1995.

Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989).

P.N.Suganathan, N.Hansen, J.J.Liang, K.Dev,Y.PChen, A.Auger,S.Tiwari,Problems Definition and Evolution Criteria for the CEC 2005 Special Session on Real-Parameter Optimization.

Pant, Millie, RadhaThangaraj, and Ajith Abraham. "A new pso algorithm with crossover operator for global optimization problems." Innovations in Hybrid Intelligent Systems. Springer Berlin Heidelberg, 2007. 215-222. DOI: https://doi.org/10.1007/978-3-540-74972-1_29

Pant, Millie, RadhaThangaraj, and V. P. Singh. "Particle swarm optimization with crossover operator and its engineering applications." IAENG International Journal of Computer Science 36.2 (2009): 112-121.

Zhang, Jian. "Particle swarm optimization using crossover operator." Journal of Convergence Information Technology 7.4 (2012): 287-295. DOI: https://doi.org/10.4156/jcit.vol7.issue4.35

Yu-hui Shi and Russell Eberhart,” A modified particle swarm optimizer,” in proceedings of IEEE world Congress on Computation intelligence, pp.69-73,1998.

Takahashi, Masato, and Hajime Kita. “A crossover operator using independent component analysis for real-coded genetic algorithms." Evolutionary Computation, 2001. Proceedings of the 2001 Congress on. Vol. 1. IEEE, 2001.

Zhi-Feng Hao,Zhi-Gang Wang,HanHuang,A Particle Swarm Optimization Algorithm With Crossover Operator Proceedings of the 6th international conference on Machine Learning and Cybernatics ,Hong Kong ,19-22 August 2007.

Chen, Stephen. "Particle swarm optimization with pbest crossover. "Evolutionary Computation (CEC), 2012 IEEE Congress on. IEEE, 2012. DOI: https://doi.org/10.1109/CEC.2012.6256497

Qinghai bai, China” Analysis of Particle Optimization Algorithm” computer and information science (CCSE), Vol. 3, No. 1, Februry 2010. DOI: https://doi.org/10.5539/cis.v3n1p180

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

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Published

2018-06-30

How to Cite

Lenin, K. (2018). CROWDING DISTANCE BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM. International Journal of Research -GRANTHAALAYAH, 6(6), 226–237. https://doi.org/10.29121/granthaalayah.v6.i6.2018.1369

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