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Binary shuffled frog leaping algorithm for optimal allocation of power quality monitors in unbalanced distribution system

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Języki publikacji
EN
Abstrakty
EN
This paper deals with optimal detection of number and best locations of power quality monitors (PQMs) in an unbalanced distribution network based on the monitor reach area concept. The proposed model uses binary string, representing the installation mode of PQMs (Yes or No) in each bus of the network. In this paper, the binary version of shuffled frog-leaping algorithm (BSFLA), because of having the ability to improve the search capability with a fast convergence rate, is utilized for the optimization process. The overall cost function is formulated to optimize the two indices, which are the monitor overlapping index and sag severity index. The only optimization constraint in this problem is that the number of monitors that can detect voltage sags due to a fault at a specific bus must not be zero. In this study, DIGSILENT software is utilized for fault analysis while the optimization problem is handled by the BSFLA. To verify the proposed algorithm, the IEEE 34 Bus unbalanced distribution network is considered as a case study and results are compared to similar investigations so as to illustrate the effectiveness of the proposed algorithm.
Twórcy
  • Department of Electrical Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
  • Department of Electrical Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
Bibliografia
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  • [2] A. Kazemi, A. Mohamed, H. Shareef, and H. Zayandehroodi. “A review of power quality monitor placement methods in transmission and distribution systems,” Przegląd elektrotechniczny, vol. 89, no. 3 A, pp. 185–188, 2013.
  • [3] D. J. Won, I. Y. Chung, J. M. Kim, S. I. Moon, J. C. Seo, and J. W. Choe. “A new algorithm to locate power-quality event source with improved realization of distributed monitoring scheme,”IEEE transactions on power delivery, vol. 21, no. 3, pp. 1641–1647, 2006, doi: 10.1109/TPWRD.2005.858810.
  • [4] D. J. Won and S. I. Moon. “Optimal Number and Locations of Power Quality Monitors Considering System Topology,” IEEE transactions on power delivery, vol. 23, no. 1, pp. 288–295, 2008, doi: 10.1109/TPWRD.2007.911126.
  • [5] M. A. Eldery, E. F. El-Saadany, M. M. A. Salama, and A. Vannelli. “A novel power quality monitoring allocation algorithm,” IEEE transactions on power delivery, vol. 21, no. 2, pp. 768–777, 2006, doi: 10.1109/TPWRD.2005.864045.
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  • [8] A. Kazemi, A. Mohamed, and H. Shareef. “A new power quality monitor placement method using the multivariable regression model and statistical indices,” INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, vol. 6, no. 5, pp. 2530–2536, 2011.
  • [9] A. Kazemi, A. Mohamed, and H. Shareef. “A novel PQM placement method using Cp and Rp statistical indices for power transmission and distribution networks,” in 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia, Jun. 2012, pp. 102–107. doi: 10.1109/PEOCO.2012.6230843.
  • [10] A. Kazemi, A. Mohamed, H. Shareef, and H. Zayandehroodi. “An improved power quality monitor placement method using MVR model and combine Cp and Rp statistical indices,” Przegl¹d elektrotechniczny, vol. 88, no. 8, pp. 205–209, 2012.
  • [11] A. Kazemi, A. Mohamed, H. Shareef, and H. Zayandehroodi. “Optimal power quality monitor placement using genetic algorithm and Mallow’s Cp,” International journal of electrical power & Energy systems, vol. 53, no. 1, pp. 564–575, 2013, doi: 10.1016/j.ijepes.2013.05.026.
  • [12] A. Kazemi, A. Mohamed, H. Shareef, and H. Raihi. “Optimal Power Quality Monitor Placement Using GACp Method for Distribution Network,” 2013. Accessed: Jan. 17, 2024. [Online]. Available: https://www.semanticscholar.org/paper/Optimal-Power-Quality-Monitor-Placement-Using-GACp-Kazemi-Mohamed/d69169ce58deabed3ff868c3bb5e70a11038797b.
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  • [14] A. A. Ibrahim, A. Mohamed, H. Shareef, and S. P. Ghoshal. “Optimal placement of voltage sag monitors based on monitor reach area and sag severity index,” in 2010 IEEE Student Conference on Research and Development (SCOReD), Dec. 2010, pp. 467–470. doi: 10.1109/SCORED.2010.5704055.
  • [15] M. Haghbin and E. Farjah. “Optimal placement of monitors in transmission systems using fuzzy boundaries for voltage sag assessment,” in 2009 IEEE Bucharest PowerTech, Jun. 2009, pp. 1–6. doi: 10.1109/PTC.2009.5281883.
  • [16] W. Hong, L. Dan, H.Wenqing, and D. Yuxing. “Optimal allocation of power quality monitors based on an improved adaptive genetic algorithm,” presented at the 2015 Joint International Mechanical, Electronic and Information Technology Conference (JIMET-15), Atlantis Press, Dec. 2015, pp. 774–785. doi: 10.2991/jimet-15.2015.145.
  • [17] A. A. Ibrahim, A. Mohamed, and H. Shareef. “Optimal placement of power quality monitors in distribution systems using the topological monitor reach area,” in 2011 IEEE International Electric Machines & Drives Conference (IEMDC), May 2011, pp. 394–399. doi: 10.1109/IEMDC.2011.5994627.
  • [18] A. A. Ibrahim, A. Mohamed, H. Shareef, and S. P. Ghoshal. “Optimal power quality monitor placement in power systems based on particle swarm optimization and artificial immune system,” in 2011 3rd Conference on Data Mining and Optimization (DMO), Jun. 2011, pp. 141–145. doi: 10.1109/DMO.2011.5976518.
  • [19] A. A. Ibrahim, A. Mohamed, H. Shareef, and S. P. Ghoshal. “An effective power quality monitor placement method utilizing quantum-inspired particle swarm optimization,” in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, Jul. 2011, pp. 1–6. doi: 10.1109/ICEEI.2011.6021845.
  • [20] A. A. Ibrahim, A. Mohamed, and H. Shareef. “A novel power quality monitor placement method using adaptive quantum-inspired binary particle swarm optimization,” Renewable Energy and Power Quality Journal, vol. 1, no. 10, pp. 50–56, Apr. 2012, doi: 10.24084/repqj10.212.
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  • [22] G. Olguin, F. Vuinovich, and M. H. J. Bollen. “An optimal monitoring program for obtaining Voltage sag system indexes,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 378–384, Feb. 2006, doi: 10.1109/TPWRS.2005.857837.
  • [23] A. A. Ibrahim, A. Mohamed, H. Shareef, and S. P. Ghoshal. “A new approach for optimal power quality monitor placement in power system considering system topology,” Przegl¹d elektrotechniczny, vol. 88, no. 9 A, pp. 272–276, 2012.
  • [24] M. Eusuff, K. Lansey, and F. Pasha. “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, vol. 38, no. 2, pp. 129–154, Mar. 2006, doi: 10.1080/03052150500384759.
  • [25] M. M. Eusuff and K. E. Lansey. “Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm,” Journal of Water Resources Planning and Management, vol. 129, no. 3, pp. 210–225, May 2003, doi: 10.1061/(ASCE)0733-9496(2003)129:3(210).
  • [26] M. Barati and M. M. Farsangi. “Solving unit commitment problem by a binary shuffled frog leaping algorithm,” IET Generation, Transmission & Distribution, vol. 8, no. 6, pp. 1050–1060, 2014, doi: 10.1049/iet-gtd.2013.0436.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-44ce074b-7fab-4288-a6fd-77183a9fd583
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