{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:22:22Z","timestamp":1783099342815,"version":"3.54.6"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51907035"],"award-info":[{"award-number":["51907035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52167007"],"award-info":[{"award-number":["52167007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Fault section location (FSL) plays a critical role in shortening blackout time and restoring power supply for distribution networks. This paper converts the FSL task into a binary optimization problem using the feeder terminal unit (FTU) information. The discrepancy between the reported overcurrent alarms and the expected overcurrent states of the FTUs is adopted as the objective function. It is a typical 0\u20131 combinatorial optimization problem with many local optima. An improved binary gaining\u2013sharing knowledge-based algorithm (IBGSK) with mutation is proposed to effectively solve this challenging binary optimization problem. Since the original GSK cannot be applied in binary search space directly, and it is easy to get stuck in local optima, IBGSK encodes the individuals as binary vectors instead of real vectors. Moreover, an improved junior gaining and sharing phase and an improved senior gaining and sharing phase are designed to update individuals directly in binary search space. Furthermore, a binary mutation operator is presented and integrated into IBGSK to enhance its global search ability. The proposed algorithm is applied to two test systems, i.e. the IEEE 33-bus distribution network and the USA PG&amp;E 69-bus distribution network. Simulation results indicate that IBGSK outperforms the other 12 advanced algorithms and the original GSK in solution quality, robustness, convergence speed, and statistics. It equilibrates the global search ability and the local search ability effectively. It can diagnose different fault scenarios with 100% and 99% success rates for these two test systems, respectively. Besides, the effect of mutation probability on IBGSK is also investigated, and the result suggests a moderate value. Overall, simulation results demonstrate that IBGSK shows highly promising potential for the FSL problem of distribution networks.<\/jats:p>","DOI":"10.1093\/jcde\/qwac007","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T20:10:07Z","timestamp":1641845407000},"page":"393-405","source":"Crossref","is-referenced-by-count":22,"title":["Improved binary gaining\u2013sharing knowledge-based algorithm with mutation for fault section location in distribution networks"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-7315","authenticated-orcid":false,"given":"Guojiang","family":"Xiong","sequence":"first","affiliation":[{"name":"Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xufeng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5895-2632","authenticated-orcid":false,"given":"Ali Wagdy","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt"},{"name":"Department of Mathematics and Actuarial Science, School of Sciences & Engineering, The American University in Cairo, New Cairo 11835, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"2022022603110908900_bib1","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1007\/s00521-020-05375-8","article-title":"A novel binary gaining\u2013sharing knowledge-based optimization algorithm for feature selection","volume":"33","author":"Agrawal","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"2022022603110908900_bib2","article-title":"Optimal feature selection using binary teaching learning-based optimization algorithm","author":"Allam","year":"2018","journal-title":"Journal of King Saud University \u2013 Computer and Information Sciences"},{"key":"2022022603110908900_bib3","doi-asserted-by":"crossref","first-page":"105576","DOI":"10.1016\/j.asoc.2019.105576","article-title":"JayaX: Jaya algorithm with XOR operator for binary optimization","volume":"82","author":"Aslan","year":"2019","journal-title":"Applied Soft Computing"},{"key":"2022022603110908900_bib4","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1016\/j.epsr.2009.04.002","article-title":"A Petri net-based protection monitoring system for distribution networks with distributed generation","volume":"79","author":"Calderaro","year":"2009","journal-title":"Electric Power Systems Research"},{"key":"2022022603110908900_bib5","first-page":"20032","article-title":"Research on distribution network fault location based on binary particle swarm optimization","volume-title":"Proceedings of the AIP Conference Proceedings","author":"Cao","year":"2019"},{"key":"2022022603110908900_bib6","first-page":"256","article-title":"Chaotic particle swarm optimization algorithm for fault location of distribution network with DG","volume-title":"Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISI)","author":"Chang","year":"2021"},{"key":"2022022603110908900_bib7","first-page":"1","article-title":"Fault location in the distribution network based on scattered measurement in the network","author":"Dashtdar","year":"2021","journal-title":"Energy Systems"},{"key":"2022022603110908900_bib10","article-title":"An enhanced MSIQDE algorithm with novel multiple strategies for global optimization problems","author":"Deng","year":"2020","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"3","key":"2022022603110908900_bib11","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1504\/IJBIC.2020.111267","article-title":"An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application","volume":"16","author":"Deng","year":"2020","journal-title":"International Journal of Bio-Inspired Computing"},{"key":"2022022603110908900_bib12","doi-asserted-by":"crossref","DOI":"10.1109\/TITS.2020.3025796","article-title":"A novel gate resource allocation method using improved PSO-based QEA","author":"Deng","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"2022022603110908900_bib8","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1007\/s00500-020-05527-x","article-title":"An improved differential evolution algorithm and its application in optimization problem","volume":"25","author":"Deng","year":"2021","journal-title":"Soft Computing"},{"key":"2022022603110908900_bib9","doi-asserted-by":"crossref","first-page":"107080","DOI":"10.1016\/j.knosys.2021.107080","article-title":"Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization","volume":"224","author":"Deng","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"2022022603110908900_bib13","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.1109\/CAC.2017.8243663","article-title":"Fault-section location of distribution network containing distributed generation based on the multiple-population genetic algorithm of chaotic optimization","volume-title":"Proceedings of the 2017 Chinese Automation Congress (CAC)","author":"Gong","year":"2017"},{"key":"2022022603110908900_bib14","first-page":"97","article-title":"Fault section location in distribution network by means of sine cosine algorithm","volume":"45","author":"Guo","year":"2017","journal-title":"Power System Protection and Control"},{"key":"2022022603110908900_bib15","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.rser.2017.03.021","article-title":"Fault location and detection techniques in power distribution systems with distributed generation: A review","volume":"74","author":"Gururajapathy","year":"2017","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"2022022603110908900_bib16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.future.2017.05.044","article-title":"A novel binary artificial bee colony algorithm for the set-union knapsack problem","volume":"78","author":"He","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"2022022603110908900_bib17","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1049\/gtd2.12324","article-title":"Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network","volume":"16","author":"Hou","year":"2022","journal-title":"IET Generation, Transmission & Distribution"},{"key":"2022022603110908900_bib18","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1109\/ACCESS.2019.2962276","article-title":"Fault location of distribution network base on improved cuckoo search algorithm","volume":"8","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"2022022603110908900_bib19","first-page":"79","article-title":"S-shaped binary whale optimization algorithm for feature selection","volume-title":"Recent trends in signal and image processing. Advances in intelligent systems and computing","author":"Hussien","year":"2019"},{"key":"2022022603110908900_bib20","first-page":"485","article-title":"Fault section location method based on fuzzy self-correction bat algorithm in non-solidly earthed distribution network","volume-title":"Proceedings of the 2nd International Conference on Power and Renewable Energy (ICPRE)","author":"Jiang","year":"2017"},{"key":"2022022603110908900_bib21","doi-asserted-by":"crossref","first-page":"14221","DOI":"10.1007\/s00500-020-04790-2","article-title":"A binary differential evolution algorithm for airline revenue management: A case study","volume":"24","author":"Karbassi\u00a0Yazdi","year":"2020","journal-title":"Soft Computing"},{"key":"2022022603110908900_bib22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TSG.2011.2118774","article-title":"Smart fault location for smart grids","volume":"2","author":"Kezunovic","year":"2011","journal-title":"IEEE Transactions on Smart Grid"},{"key":"2022022603110908900_bib23","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TSG.2019.2917506","article-title":"Fault section identification in smart distribution systems using multi-source data based on fuzzy Petri nets","volume":"11","author":"Kiaei","year":"2019","journal-title":"IEEE Transactions on Smart Grid"},{"key":"2022022603110908900_bib24","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.pnsc.2008.03.018","article-title":"Modified binary particle swarm optimization","volume":"18","author":"Lee","year":"2008","journal-title":"Progress in Natural Science"},{"issue":"8","key":"2022022603110908900_bib25","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1615\/TelecomRadEng.v79.i8.80","article-title":"Research on fault location of power distribution network based on fault data information","volume":"79","author":"Li","year":"2020","journal-title":"Telecommunications and Radio Engineering"},{"key":"2022022603110908900_bib26","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/TNB.2013.2294716","article-title":"Multiobjective binary biogeography based optimization for feature selection using gene expression data","volume":"12","author":"Li","year":"2013","journal-title":"IEEE Transactions on Nanobioscience"},{"key":"2022022603110908900_bib27","first-page":"2160","article-title":"Fault location method of distribution network based on fruit fly optimization algorithm","volume-title":"Proceedings of the 16th IET International Conference on AC and DC Power Transmission (ACDC 2020)","author":"Li","year":"2020"},{"key":"2022022603110908900_bib28","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/CAC51589.2020.9327054","article-title":"Fault location of active distribution network based on improved gray wolf algorithm","volume-title":"Proceedings of the 2020 Chinese Automation Congress (CAC)","author":"Li","year":"2020"},{"key":"2022022603110908900_bib29","first-page":"012106","article-title":"Research on IACA of distribution network fault location","volume":"1654","author":"Liu","year":"2020","journal-title":"Journal of Physics: Conference Series"},{"key":"2022022603110908900_bib30","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","article-title":"Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems","volume":"27","author":"Mirjalili","year":"2016","journal-title":"Neural Computing & Applications"},{"key":"2022022603110908900_bib31","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s00521-013-1525-5","article-title":"Binary bat algorithm","volume":"25","author":"Mirjalili","year":"2014","journal-title":"Neural Computing & Applications"},{"key":"2022022603110908900_bib32","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s13042-019-01053-x","article-title":"Gaining\u2013sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm","volume":"11","author":"Mohamed","year":"2020","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"2022022603110908900_bib33","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ijepes.2016.06.002","article-title":"Active distribution network fault location methodology: A minimum fault reactance and Fibonacci search approach","volume":"84","author":"Orozco-Henao","year":"2017","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"2022022603110908900_bib34","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1080\/15325008.2017.1310772","article-title":"A review on distribution grid fault location techniques","volume":"45","author":"Shafiullah","year":"2017","journal-title":"Electric Power Components and Systems"},{"key":"2022022603110908900_bib35","first-page":"109","article-title":"Fault section location for distribution network with DG based on binary hybrid algorithm","volume-title":"Proceedings of International Conference on Mechanical and Mechatronics Engineering (ICMME)","author":"Shi","year":"2017"},{"key":"2022022603110908900_bib36","first-page":"1220","article-title":"Fault-section location of distribution network based on adaptive mutation shuffled frog leaping algorithm","volume":"15","author":"Sun","year":"2019","journal-title":"International Journal of Performability Engineering"},{"key":"2022022603110908900_bib37","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/17445760.2019.1682145","article-title":"Fault location of distribution network with distributed generations using electrical synaptic transmission-based spiking neural P systems","volume":"36","author":"Sun","year":"2021","journal-title":"International Journal of Parallel, Emergent and Distributed Systems"},{"key":"2022022603110908900_bib39","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1109\/IAEAC.2018.8577707","article-title":"Application of improved quantum genetic algorithm in fault location of distribution network","volume-title":"Proceedings of the 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC 2018)","author":"Wang","year":"2018"},{"key":"2022022603110908900_bib38","doi-asserted-by":"crossref","first-page":"30683","DOI":"10.1109\/ACCESS.2019.2902598","article-title":"An innovative minimum hitting set algorithm for model-based fault diagnosis in power distribution network","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"2022022603110908900_bib47","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.solener.2018.10.050","article-title":"Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm","volume":"176","author":"Xiong","year":"2018","journal-title":"Solar Energy"},{"key":"2022022603110908900_bib42","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/978-3-030-15070-9_3","article-title":"Oppositional brain storm optimization for fault section location in distribution networks","volume-title":"Brain storm optimization algorithms. Adaptation, learning, and optimization","author":"Xiong","year":"2019"},{"key":"2022022603110908900_bib46","doi-asserted-by":"crossref","first-page":"112450","DOI":"10.1016\/j.enconman.2019.112450","article-title":"Winner-leading competitive swarm optimizer with dynamic Gaussian mutation for parameter extraction of solar photovoltaic models","volume":"206","author":"Xiong","year":"2020","journal-title":"Energy Conversion and Management"},{"key":"2022022603110908900_bib43","doi-asserted-by":"crossref","first-page":"113395","DOI":"10.1016\/j.enconman.2020.113395","article-title":"Parameter extraction of solar photovoltaic models with an either-or teaching learning based algorithm","volume":"224","author":"Xiong","year":"2020","journal-title":"Energy Conversion and Management"},{"key":"2022022603110908900_bib44","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.1039\/D0SE01000F","article-title":"Parameter extraction of solar photovoltaic models via quadratic interpolation learning differential evolution","volume":"4","author":"Xiong","year":"2020","journal-title":"Sustainable Energy & Fuels"},{"key":"2022022603110908900_bib40","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1016\/j.egyr.2021.05.030","article-title":"A new method for parameter extraction of solar photovoltaic models using gaining\u2013sharing knowledge-based algorithm","volume":"7","author":"Xiong","year":"2021","journal-title":"Energy Reports"},{"issue":"9","key":"2022022603110908900_bib45","doi-asserted-by":"crossref","first-page":"6720","DOI":"10.1016\/j.ijhydene.2020.11.119","article-title":"Optimal identification of solid oxide fuel cell parameters using a competitive hybrid differential evolution and Jaya algorithm","volume":"46","author":"Xiong","year":"2021","journal-title":"International Journal of Hydrogen Energy"},{"key":"2022022603110908900_bib41","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1002\/int.22659","article-title":"Fault section diagnosis of power systems with logical operation binary gaining\u2013sharing knowledge-based algorithm","volume":"37","author":"Xiong","year":"2022","journal-title":"International Journal of Intelligent Systems"},{"key":"2022022603110908900_bib48","first-page":"02010","article-title":"A modified matrix algorithm dichotomy for distribution network fault location","volume-title":"Proceedings of the 2020 International Conference of Recent Trends in Environmental Sustainability and Green Technologies (ICRTEG 2020)","author":"Yan","year":"2020"},{"issue":"1","key":"2022022603110908900_bib49","doi-asserted-by":"crossref","first-page":"143","DOI":"10.23940\/ijpe.20.01.p15.143151","article-title":"Fault section location of active distribution network based on wolf pack and differential evolution algorithms","volume":"16","author":"Yang","year":"2020","journal-title":"International Journal of Performability Engineering"},{"issue":"3","key":"2022022603110908900_bib50","first-page":"472","article-title":"Fault location in distribution system using convolutional neural network based on domain transformation","volume":"7","author":"Yu","year":"2021","journal-title":"CSEE Journal of Power and Energy Systems"},{"key":"2022022603110908900_bib51","doi-asserted-by":"crossref","first-page":"106870","DOI":"10.1016\/j.epsr.2020.106870","article-title":"Fault diagnosis method of distribution network based on time sequence hierarchical fuzzy petri nets","volume":"191","author":"Yuan","year":"2021","journal-title":"Electric Power Systems Research"},{"key":"2022022603110908900_bib52","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1080\/15325008.2018.1460884","article-title":"Fault diagnosis and location method for active distribution network based on artificial neural network","volume":"46","author":"Zhang","year":"2018","journal-title":"Electric Power Components and Systems"},{"key":"2022022603110908900_bib53","doi-asserted-by":"crossref","first-page":"937","DOI":"10.3233\/JCM-204231","article-title":"Fault section location for distribution network containing DG based on IBQPSO","volume":"20","author":"Zhao","year":"2020","journal-title":"Journal of Computational Methods in Sciences and Engineering"},{"key":"2022022603110908900_bib54","first-page":"592","article-title":"Distribution network fault segment location algorithm based on Bayesian estimation in intelligent distributed control mode","volume-title":"Proceedings of the 2020 Asia Energy and Electrical Engineering Symposium (AEEES)","author":"Zheng","year":"2020"},{"key":"2022022603110908900_bib55","first-page":"012016","article-title":"Fault location for multi-source distribution network based on improved chaotic Jaya algorithm","volume":"2095","author":"Zhou","year":"2021","journal-title":"Journal of Physics: Conference Series"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/393\/42616816\/qwac007.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/9\/2\/393\/42616816\/qwac007.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T13:40:47Z","timestamp":1674394847000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/9\/2\/393\/6537179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,25]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2,25]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwac007","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,4]]},"published":{"date-parts":[[2022,2,25]]}}}