Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population

The 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...

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Autores:
Quintero Durán, Michell Josep
Candelo Becerra, John Edwin
Cabana Jiménez, Katherine
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5655
Acceso en línea:
https://hdl.handle.net/11323/5655
https://repositorio.cuc.edu.co/
Palabra clave:
Bat algorithm
Modified binary bat algorithm
Power loss minimization
Power optimization
Reconfiguration
Seed population
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_cb4db74ae82fbfb1b10a76b81b047f19
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5655
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
title Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
spellingShingle Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
Bat algorithm
Modified binary bat algorithm
Power loss minimization
Power optimization
Reconfiguration
Seed population
title_short Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
title_full Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
title_fullStr Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
title_full_unstemmed Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
title_sort Distribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed population
dc.creator.fl_str_mv Quintero Durán, Michell Josep
Candelo Becerra, John Edwin
Cabana Jiménez, Katherine
dc.contributor.author.spa.fl_str_mv Quintero Durán, Michell Josep
Candelo Becerra, John Edwin
Cabana Jiménez, Katherine
dc.subject.spa.fl_str_mv Bat algorithm
Modified binary bat algorithm
Power loss minimization
Power optimization
Reconfiguration
Seed population
topic Bat algorithm
Modified binary bat algorithm
Power loss minimization
Power optimization
Reconfiguration
Seed population
description The 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 switches.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-11-14T19:46:51Z
dc.date.available.none.fl_str_mv 2019-11-14T19:46:51Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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identifier_str_mv 1330-3651
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Corporación Universidad de la Costa
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.17559/TV-20180525204445
dc.relation.references.spa.fl_str_mv [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, https://doi.org/10.1016/j.epsr.2010.01.001 80(8), 943–953. [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-s2.00031352450&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), 1044–1051. https://doi.org/10.1109/59.207317
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spelling Quintero Durán, Michell JosepCandelo Becerra, John EdwinCabana Jiménez, Katherine2019-11-14T19:46:51Z2019-11-14T19:46:51Z20191330-36511848-6339https://hdl.handle.net/11323/5655Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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 switches.Quintero Durán, Michell Josep-will be generated-orcid-0000-0003-1406-9888-600Candelo Becerra, John Edwin-will be generated-orcid-0000-0002-9784-9494-600Cabana Jiménez, Katherine-will be generated-orcid-0000-0003-3859-1160-600engTehnicki vjesnikhttps://doi.org/10.17559/TV-20180525204445[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, https://doi.org/10.1016/j.epsr.2010.01.001 80(8), 943–953. [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-s2.00031352450&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), 1044–1051. https://doi.org/10.1109/59.207317CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bat algorithmModified binary bat algorithmPower loss minimizationPower optimizationReconfigurationSeed populationDistribution network reconfiguration with large number of switches solved by a modified binary bat algorithm and improved seed populationArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALDistribution Network Reconfiguration with Large Number.pdfDistribution Network Reconfiguration with Large Number.pdfapplication/pdf957713https://repositorio.cuc.edu.co/bitstreams/03e83da3-da2c-44c2-aa83-e49a4594e9ea/download3d9b3b2238171231306c6a5daf566e82MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/da5ac300-15f2-4397-ba5a-2a49c8e92e9d/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/4ab4e3e0-9f6e-480f-9cf1-89fc1dcd3cfd/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILDistribution Network Reconfiguration with Large Number.pdf.jpgDistribution Network Reconfiguration with Large Number.pdf.jpgimage/jpeg79799https://repositorio.cuc.edu.co/bitstreams/f9a0ae87-713d-4dfe-bfec-0fb4fd3180fb/download7c74bd0f9a8b06b2db6e878e3d5724b9MD55TEXTDistribution Network Reconfiguration with Large Number.pdf.txtDistribution Network Reconfiguration with Large Number.pdf.txttext/plain45139https://repositorio.cuc.edu.co/bitstreams/ce0f8798-7688-4a57-944d-a6ac98a7bc4b/download97fa1bb9d91f1554a898a51207db46c7MD5611323/5655oai:repositorio.cuc.edu.co:11323/56552024-09-17 14:10:29.324http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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