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dc.contributor.authorQuintero Durán, Michell Josepspa
dc.contributor.authorCandelo Becerra, John Edwinspa
dc.contributor.authorCabana Jiménez, Katherinespa
dc.date.accessioned2019-11-13T22:00:41Z
dc.date.available2019-11-13T22:00:41Z
dc.date.issued2019
dc.identifier.issn1848-6339spa
dc.identifier.urihttp://hdl.handle.net/11323/5650spa
dc.description.abstractThe paper presents a methodology based on a Modified Binary Bat Algorithm (MBBA) and Improved Seed Population search that provides nearly optimal solutions to the power loss minimization problem, considering network reconfiguration and a large number of switches. The existence of many switches leads to a very large number of combinations, making it hard for algorithms to find a good solution. The proposed method is based on eliminating non-feasible solutions and defining an initial matrix with improved seed population for searching the optimal solution. This seed is used for the random process of the algorithm to produce new solutions and is continually updated to obtain better results close to the optimal solutions found during the searching process of the metaheuristic algorithm. This algorithm was tested against the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Seed Population search alone on the modified versions of the IEEE 13-node test and IEEE 123-node test feeders. From several runs, the proposed method reached the optimal solution more times than the other algorithms and the remainder achieved near-optimal solutions. With this result, the MBBA provides good options to improve the solutions in the network reconfiguration problem with a large number of switchesspa
dc.language.isoeng
dc.publisherTehnički vjesnikspa
dc.relation.ispartofhttps://doi.org/10.17559/TV-20180525204445spa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectMat algorithmspa
dc.subjectModified binary bat algorithmspa
dc.subjectPower loss minimizationspa
dc.subjectPower optimizationspa
dc.subjectReconfigurationspa
dc.subjectSeed populationspa
dc.titleDistribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed populationspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references[1] Nguyen, T. T., Nguyen, T. T., Truong, A. V., Nguyen, Q. T., & Phung, T. A. (2017). Multi-objective electric distribution network reconfiguration solution using runner-root algorithm. Applied Soft Computing, 52, 93–108. https://doi.org/10.1016/j.asoc.2016.12.018 [2] Abdelaziz, A. Y., Osama, R. A., & Elkhodary, S. M. (2013). Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms. Electric Power Components and Systems, 41(5), 537–554. https://doi.org/10.1080/15325008.2012.755232 [3] Herazo, E., Quintero, M., Candelo, J., Soto, J., & Guerrero, J. (2015). Optimal power distribution network reconfiguration using Cuckoo Search. In The 4th International Conference on Electric Power and Energy Conversion Systems (EPECS) (pp. 1–6). IEEE. https://doi.org/10.1109/EPECS.2015.7368548 [4] Farahani, V., Vahidi, B., & Abyaneh, H. A. (2012). Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Method. IEEE Transactions on Power Systems, 27(2), 587–595. https://doi.org/10.1109/TPWRS.2011.2167688 [5] Garcia-Martinez, S. & Espinosa-Juarez, E. (2011). Reconfiguration of power systems by applying Tabu search to minimize voltage sag indexes. In 2011 North American Power Symposium (pp. 1–6). IEEE. https://doi.org/10.1109/NAPS.2011.6025100 [6] García-Martínez, S. & Espinosa-Juárez, E. (2013). Optimal Reconfiguration of Electrical Networks by Applying Tabu Search to Decrease Voltage Sag Indices. Electric Power Components and Systems, 41(10), 943–959. https://doi.org/10.1080/15325008.2013.801053 [7] Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing, 1(3), 190–206. https://doi.org/10.1287/ijoc.2.1.4 [8] Graditi, G., Di Silvestre, M. L., La Cascia, D., Riva Sanseverino, E., & Zizzo, G. (2016). On multi-objective optimal reconfiguration of MV networks in presence of different grounding. Journal of Ambient Intelligence and Humanized Computing, 7(1), 97–105. https://doi.org/10.1007/s12652-015-0304-9 [9] Gu, C., Ji, J., & Liu, L. (2014). Research of immune algorithms for reconfiguration of distribution network with distributed generations. In The 26th Chinese Control and Decision Conference (2014 CCDC) (pp. 2156–2160).IEEE. https://doi.org/10.1109/CCDC.2014.6852524 [10] Gupta, N., Swarnkar, A., & Niazi, K. R. (2014). Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms. International Journal of Electrical Power & Energy Systems, 54, 664–671. https://doi.org/10.1016/j.ijepes.2013.08.016 [11] Abazari, S. & Soudejani, M. H. (2015). A new technique for efficient reconfiguration of distribution networks. Scientia Iranica, 22(6), 2516–2526. [12] Abdelaziz, A. Y., Mohamed, F. M., Mekhamer, S. F., & Badr, M. A. L. (2010). Distribution system reconfiguration using a modified Tabu Search algorithm. Electric Power Systems Research, 80(8), 943–953. https://doi.org/10.1016/j.epsr.2010.01.001 [13] Abdelaziz, A. Y., Osama, R. A., & El-Khodary, S. M. (2012). Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm. IET Generation, Transmission & Distribution, 6(2), 176. https://doi.org/10.1049/iet-gtd.2011.0281 [14] Asrari, A., Lotfifard, S., & Ansari, M. (2016). Reconfiguration of Smart Distribution Systems with Time Varying Loads Using Parallel Computing. IEEE Transactions on Smart Grid, 1–11. https://doi.org/10.1109/TSG.2016.2530713 [15] Liu, L. H., Wang, Y., Yao, S. J., Ma, L. Y., & Yang, J. (2012). Distribution Network Reconfiguration with Distributed Generation Based on Cloud Genetic Algorithm. Advanced Materials Research, 529, 306–310. https://doi.org/10.4028/www.scientific.net/AMR.529.306 [16] Quintero-Duran, M., Candelo, J. E., & Sousa, V. (2017). Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration. Journal of Engineering Science and Technology Review, 10(5), 159– 173. https://doi.org/10.25103/jestr.105.20 [17] Mirjalili, S., Mirjalili, S. M., & Yang, X.-S. (2014). Binary bat algorithm. Neural Computing and Applications, 25(3–4), 663–681. https://doi.org/10.1007/s00521-013-1525-5 [18] Amanulla, B., Chakrabarti, S., & Singh, S. N. (2012). Reconfiguration of Power Distribution Systems Considering Reliability and Power Loss. IEEE Transactions on Power Delivery, 27(2), 918–926. https://doi.org/10.1109/TPWRD.2011.2179950 [19] Alonso, F. R., Oliveira, D. Q., & Zambroni de Souza, A. C. (2015). Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration. IEEE Transactions on Power Systems, 30(2), 840–847. https://doi.org/10.1109/TPWRS.2014.2330628 [20] Yang, X.-S. (2010). A New Metaheuristic Bat-Inspired Algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2011) (Vol. 284, pp. 65–74). https://doi.org/10.1007/978-3-642-12538-6_6 [21] Peres, W., Silva Júnior, I. C., & Passos Filho, J. A. (2018). Gradient based hybrid metaheuristics for robust tuning of power system stabilizers. International Journal of Electrical Power & Energy Systems, 95, 47–72. https://doi.org/10.1016/j.ijepes.2017.08.014 [22] Niu, T., Wang, J., Zhang, K., & Du, P. (2018). Multi-stepahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy. Renewable Energy, 118, 213–229. https://doi.org/10.1016/j.renene.2017.10.075 [23] Montgomery, D. C. (2006). Design and Analysis of Experiments. Technometrics (Vol. 48). https://doi.org/10.1198/tech.2006.s372 [24] Quintero-Duran, M., Candelo-Becerra, J. E., & Soto-Ortiz, J. D. (2019). A Modified Backward/Forward Sweep-based Method for Reconfiguration of Unbalanced Distribution Networks. International Journal of Electrical and Computer Engineering, 9(1), 85-101. https://doi.org/10.11591/ijece.v9i1.pp.85-101 [25] Kennedy, J., & Eberhart, R. C. (1997). Discrete binary version of the particle swarm algorithm. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 5, pp. 4104–4108). Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0- 0031352450&partnerID=40&md5=a713161d139c8afba89f 6b67c67696c7 [26] Nara, K., Shiose, A., Kitagawa, M., & Ishihara, T. (1992). Implementation of genetic algorithm for distribution systems loss minimum re-configuration. IEEE Transactions on Power Systems, 7(3), 1044–1051spa
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