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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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A Phasor Estimation Algorithm based on Hilbert Transform for P-class PMUs, RAZO-HERNANDEZ, J. R., VALTIERRA-RODRIGUEZ, M., GRANADOS-LIEBERMAN, D., TAPIA-TINOCO, G., RODRIGUEZ-RODRIGUEZ, J. R.
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  4/2018 - 5

Methods of Simulated Annealing and Particle Swarm Applied to the Optimization of Reactive Power Flow in Electric Power Systems

PIJARSKI, P. See more information about PIJARSKI, P. on SCOPUS See more information about PIJARSKI, P. on IEEExplore See more information about PIJARSKI, P. on Web of Science, KACEJKO, P. See more information about KACEJKO, P. on SCOPUS See more information about KACEJKO, P. on SCOPUS See more information about KACEJKO, P. on Web of Science
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Download PDF pdficon (1,196 KB) | Citation | Downloads: 260 | Views: 389

Author keywords
optimization, heuristic algorithms, power systems, reactive power control, compensation

References keywords
power(12), optimization(7), systems(6), swarm(6), fuzzy(4), control(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 43 - 48
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04005
Web of Science Accession Number: 000451843400005
SCOPUS ID: 85058812305

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Electric power system is characterized by relatively high demand for lagging reactive power. From the economic viewpoint, reactive power sources should be installed close to its demand. Optimal compensation should ensure minimal costs of the reactive power generation and transmission within the considered system. The optimization of activities related to reactive power compensation concerns the location and power of compensation devices. This is to optimize voltage levels and reactive power flows in the system. The article presents methods of simulated annealing and particle swarm applied to solve an optimization task of the reactive power flow. It has been assumed that active power losses in a power system are the objective function.

References | Cited By  «-- Click to see who has cited this paper

[1] A. Meier, "Electric Power Systems: Conceptual Introduction", pp. 144-228, Wiley-IEEE Press, 2006.

[2] J. Machowski, J. Bialek, J. Bumby, "Power system dynamics stability and control", pp. 15-122, John Wiley & Sons, 2008.

[3] L. L. Grigsby, "Power systems", pp. 46-56, CRC Press, 2012.

[4] J. Zhu, "Optimization of Power System Operation", pp. 1-50, Wiley-IEEE Press, 2015.

[5] W. Zhang, Y. Liu, "Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm," International Journal of Electrical Power & Energy Systems, vol. 30, no. 9, pp. 525-532, 2008.
[CrossRef] [Web of Science Times Cited 111]

[6] C. Wang, G. Yao, X. Wang et al., "Reactive Power Optimization Based on Particle Swarm Optimization Algorithm in 10kV Distribution Network," Advances in Swarm Intelligence, vol. 6728, pp. 157-164, 2011.

[7] A. Q. H. Badar, B. S. Umre,, A. S. Junghare, "Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization," International Journal of Electrical Power & Energy Systems, vol. 41, no. 1, pp. 133-136, 2012.
[CrossRef] [Web of Science Times Cited 70]

[8] S. Biswas, K. K. Manadal, N. Chakraborty, "Simulated Annealing Based Real Power Loss Minimization Aspect for a Large Power Network," Swarm, Evolutionary, and Memetic Computing, vol. 8297, pp. 345-353, 2013.

[9] M. A. Abido, "Multiobjective Optimal VAR Dispatch Using Strength Pareto Evolutionary Algorithm," 2006 IEEE International Conference on Evolutionary Computation, Vancouver, pp. 16-21, 2006.
[CrossRef] [Web of Science Times Cited 17]

[10] M. S. Bazaraa, H. D. Sherali, C. M. Shetty, "Nonlinear Programming: Theory and Algorithms", pp. 1-313, Wiley, 2006.

[11] Z. Michalewicz, D. B. Fogel, "How to Solve It. Modern Heuristics", pp. 145-487, Springer , 2004.

[12] X.-S. Yang, "Nature-inspired metaheuristic algorithms", pp. 11-108, Luniver Press, 2010.

[13] P. J. Braspenning, F. Thuijsman, A. J. M. M. Weijters, "Artificial Neural Networks: An Introduction to ANN Theory and Practice", pp.1-100, Springer, 1995.

[14] W. Pedrycz, "Fuzzy control and fuzzy systems", pp. 1-78, John Wiley & Sons, 1996.

[15] L. J. Fogel, Owens, A., J., M., J. Walsh, "Artificial Intelligence through Simulated Evolution", pp. 11-66, John Wiley & Sons, 1966.

[16] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, "Optimization by Simulated Annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
[CrossRef] [Web of Science Times Cited 19874]

[17] H. Bersini, J. Varela, Francisco, "Hints for adaptive problem solving gleaned from immune networks," Parallel Problem Solving from Nature, vol. 496, pp. 343-354, 1990.

[18] R. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory," in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, IEEE, 1995.

[19] A. Colorni, M. Dorigo, V. Maniezzo, "Distributed Optimization by Ant Colonies," Appeared in Procedings of ECAL91, pp. 134-142, 1991.

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References Weight

Web of Science® Citations for all references: 20,072 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 836 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-17 06:26 in 78 seconds.

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