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

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


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  1/2020 - 2

Artificial Immunity Based Wound Healing Algorithm for Power Loss Optimization in Smart Grids

CINAR, M. See more information about CINAR, M. on SCOPUS See more information about CINAR, M. on IEEExplore See more information about CINAR, M. on Web of Science, KAYGUSUZ, A. See more information about KAYGUSUZ, A. on SCOPUS See more information about KAYGUSUZ, A. on SCOPUS See more information about KAYGUSUZ, A. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (474 KB) | Citation | Downloads: 291 | Views: 358

Author keywords
smart grids, load flow, optimization methods, power system analysis computing, power system simulation

References keywords
power(35), systems(17), reactive(15), optimal(12), dispatch(10), optimization(9), swarm(8), algorithm(8), electric(6), loss(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-02-28
Volume 20, Issue 1, Year 2020, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.01002
Web of Science Accession Number: 000518392600002
SCOPUS ID: 85083726686

Abstract
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In this study, a human immune system based wound healing algorithm is mentioned to optimize power losses in the smart grids. The smart grids are a concept that uses communication and control techniques to increase the efficiency of today's electrical systems, provide bidirectional communication and allow instant monitoring of the grid. The wound healing algorithm is computationally simulated in the event of a possible injury to the human body and there are very few publications on the proposed algorithm when the literature review is performed. Therefore, the proposed algorithm is capable of removing this gap in the literature. The codes are written in the Matlab GUI environment and applied to the IEEE 30-busbar system and power losses are tried to be optimized. Simulation results show that the actual power loss is significantly reduced. The obtained results were compared with the results of other algorithms that are available in the literature. The proposed wound healing algorithm has given more optimum and superior solutions than the other algorithms compared in terms of calculation time and optimum power loss values and it was emphasized that it was a more effective method in providing the solution.


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

[1] J. Dongli, X. Meng and X. Song, "Study on technology system of self-healing control in smart distribution grid," International Conference on Advanced Power System Automation and Protection, Beijing, 2011, pp.26-30.
[CrossRef] [SCOPUS Times Cited 39]


[2] X. Fang, S. Misra, G. Xue and D.Yang, "Smart grid-the new and improveds power gird: a survey", IEEE Communication Surveys& Tutorials, vol. 14, no. 4, pp. 944-980, 2012.
[CrossRef] [Web of Science Times Cited 1180] [SCOPUS Times Cited 1510]


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[CrossRef] [SCOPUS Times Cited 1]


[4] NIST, NIST framework and roadmap for smart grid interoperability standards, Release 3.0, 2014.

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[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 106]


[6] A survey: Algorithms stimulating bee swarm intelligence (Karaboga, Akay, 2009).

[7] T. Q. D. Khao and B. T. T. Phan, "Ant colony search-based loss minimum for reconfiguration of distributed systems," IEEE Power India Conference New Delphi, India, 2006, pp.1-6.
[CrossRef] [SCOPUS Times Cited 8]


[8] X. S. Yong and X. He, "Firefly algorithm: Recent advances and applications," International Journal of Swarm Intelligence, vol. 1, pp. 36, 2013.
[CrossRef]


[9] J. C. Cebrian and N. Kagan, "Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms," Electric Power Systems Research, vol. 80, no. 1, pp. 53-62, 2010.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 55]


[10] B. Zhao, C. X. Guo and Y. J. Cao, "A multiagent-based particle swarm optimization approach for optimal reactive power dispatch," IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 1070-1078, 2005.
[CrossRef] [Web of Science Times Cited 312] [SCOPUS Times Cited 416]


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[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 110]


[12] R. N. S. Mei, M. H. Sulaiman, Z. Mustaffa and H. Daniyal, "Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique," Applied Soft Computing, vol. 59, pp. 210-222, 2017.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 46]


[13] Y. Mao and M. Li, "Optimal reactive power planning based on simulated annealing particle swarm algorithm considering static voltage stability," International Conference on Intelligent Computation Technology and Automation (ICICTA),Hunan, 2018, pp.106-110.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


[14] Z. Wen and L. Yutian, "Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm," Electric Power and Energy Systems, vol. 30, no. 9, pp. 525-532, 2008.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 170]


[15] S. Mouassa, T. Bouktir and A. Salhi, "Ant lion optimizer for solving optimal reactive power dispatch problem in power systems," Engineering Science and Technology an International Journal, vol. 20, no. 3, pp. 885-895, 2017.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 45]


[16] D. Chaohua, C. Weirong, Z. Yunfang and Z. Xuexia, "Seeker optimization algorithm for optimal reactive power dispatch," IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1218-1231, 2009.
[CrossRef] [Web of Science Times Cited 163] [SCOPUS Times Cited 242]


[17] K. Mahadevan and P.S. Kannan, "Comprehensive learning particle swarm optimization for reactive power dispatch," Applied Soft Computing, vol. 10, no. 2, pp. 641-652, 2010.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 167]


[18] P. K. Roy, S. P. Ghoshal and S. S. Thakur, "Optimal VAR control for improvements in voltage profiles and for real power loss minimization using biogeography based optimization," Electric Power and Energy Systems, vol. 43, no. 1, pp. 830-838, 2012.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 61]


[19] K. Ayan and U. Kilic, "Artificial bee colony algorithm solution for optimal reactive power flow," Applied Soft Computing, vol. 12, no. 5, pp. 1477-1482, 2012.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 134]


[20] A. Q. Badar, B. S. Umre and A. S. Junghare, "Reactive power control using dynamic particle swarm optimization for real power loss minimization," Electric Power and Energy Systems, vol. 41, no. 1, pp. 133-136, 2012.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 89]


[21] L. N. de Castro and J.Timmis, "Artificial Immune Systems: A New Computational Intelligence Approach," Springer-Verlag, 2002.

[22] R. Belkacemi and A. Feliachi, "An immune system approach for power system automation and self healing," 2009 IEEE/PES Power Systems Conference and Exposition, Seattle, WA, 2009, pp. 1-7,
[CrossRef]


[23] S. S. F. Souza, R. Romero and J. F. Franco, "Artificial immune networks Copt-aiNet and Opt-aiNet applied to the reconfiguration problem of radial electrical distribution systems," Electric Power Systems Research, vol. 119, pp. 304-312, 2015.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 22]


[24] A. A. Abou El Ela, M. A. Abido and S. R. Spea, "Differential evolution algorithm for optimal reactive power dispatch," Electric Power Systems Research, vol. 81, no. 2, pp. 458-64, 2011.
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 160]


[25] S. Duman, Y. Sonmez, U. Guvenc and N. Yorukeren, "Optimal reactive power dispatch using a gravitational search algorithm," IET Generation, Transmission & Distribution, vol. 6, no. 6, pp. 563-576, 2012.
[CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 141]


[26] A. Bhattacharya and P. K. Chattopadhyay, "Solution of optimal reactive power flow using biogeography-based optimization," International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol. 4, no. 3, pp. 621-629, 2010.

[27] K. Mahadevan and P. S. Kannan, "Comprehensive learning particle swarm optimization for reactive power dispatch," Applied Soft Computing, vol. 10, pp. 641-652, 2010.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 167]


[28] B. Shaw, V. Mukherjee and S. P. Ghoshal, "Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm," Electrical Power and Energy Systems, vol. 55, pp. 29-40, 2014.
[CrossRef] [Web of Science Times Cited 89] [SCOPUS Times Cited 113]




References Weight

Web of Science® Citations for all references: 2,875 TCR
SCOPUS® Citations for all references: 3,815 TCR

Web of Science® Average Citations per reference: 99 ACR
SCOPUS® Average Citations per reference: 132 ACR

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 2020-07-07 19:39 in 164 seconds.




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