{"id":"https://openalex.org/W7165154202","doi":"https://doi.org/10.1016/j.array.2026.101024","title":"Fault resistance estimation and classification in 11\u00a0kV distribution networks with noise robustness: A machine learning-based multi-fault analysis","display_name":"Fault resistance estimation and classification in 11\u00a0kV distribution networks with noise robustness: A machine learning-based multi-fault analysis","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165154202","doi":"https://doi.org/10.1016/j.array.2026.101024"},"language":"en","primary_location":{"id":"doi:10.1016/j.array.2026.101024","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.101024","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.array.2026.101024","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061973550","display_name":"Sujit Kumar","orcid":"https://orcid.org/0000-0002-7408-7394"},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sujit Kumar","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0002-7408-7394","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078466185","display_name":"Anubhav Kumar Pandey","orcid":"https://orcid.org/0000-0002-0935-3262"},"institutions":[{"id":"https://openalex.org/I8977528","display_name":"Dr. Hari Singh Gour University","ror":"https://ror.org/01xapxe37","country_code":"IN","type":"education","lineage":["https://openalex.org/I8977528"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anubhav Kumar Pandey","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0002-0935-3262","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India","institution_ids":["https://openalex.org/I8977528"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126511869","display_name":"K. K. Nandini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nandini K. K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006978961","display_name":"Nikita Gupta","orcid":"https://orcid.org/0000-0003-2895-9873"},"institutions":[{"id":"https://openalex.org/I904467727","display_name":"Himachal Pradesh University","ror":"https://ror.org/02s5yma07","country_code":"IN","type":"education","lineage":["https://openalex.org/I904467727"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nikita Gupta","raw_affiliation_strings":["Department of Electrical Engineering, University Institute of Technology, Himachal Pradesh University, Shimla, India"],"raw_orcid":"https://orcid.org/0000-0003-2895-9873","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University Institute of Technology, Himachal Pradesh University, Shimla, India","institution_ids":["https://openalex.org/I904467727"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068901780","display_name":"Kama Ramudu","orcid":"https://orcid.org/0000-0002-8585-9396"},"institutions":[{"id":"https://openalex.org/I142809039","display_name":"Jawaharlal Nehru Technological University, Kakinada","ror":"https://ror.org/05s9t8c95","country_code":"IN","type":"education","lineage":["https://openalex.org/I142809039"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kama Ramudu","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Aditya University, Surampalem, Kakinada, Andhra Pradesh, 533437, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Aditya University, Surampalem, Kakinada, Andhra Pradesh, 533437, India","institution_ids":["https://openalex.org/I142809039"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091303313","display_name":"Yanxia Sun","orcid":"https://orcid.org/0000-0002-3455-9625"},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Yanxia Sun","raw_affiliation_strings":["Department of Electrical and Electronic Engineering Science University of Johannesburg, Corner of Kingsway Ave &, University Rd, Auckland Park, Johannesburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering Science University of Johannesburg, Corner of Kingsway Ave &, University Rd, Auckland Park, Johannesburg","institution_ids":["https://openalex.org/I24027795"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061973550"],"corresponding_institution_ids":["https://openalex.org/I81556334"],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87651579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"31","issue":null,"first_page":"101024","last_page":"101024"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10972","display_name":"Power Systems Fault Detection","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10972","display_name":"Power Systems Fault Detection","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12737","display_name":"Electrical Fault Detection and Protection","score":0.006000000052154064,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13183","display_name":"Islanding Detection in Power Systems","score":0.004699999932199717,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6308000087738037},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.550599992275238},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.545199990272522},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5317000150680542},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4489000141620636},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.41760000586509705},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.39070001244544983},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.38440001010894775},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.36090001463890076},{"id":"https://openalex.org/keywords/root-mean-square","display_name":"Root mean square","score":0.3474000096321106}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6308000087738037},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.550599992275238},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.545199990272522},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4406999945640564},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41200000047683716},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4009000062942505},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38440001010894775},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.36090001463890076},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3343999981880188},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3066999912261963},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C21267803","wikidata":"https://www.wikidata.org/wiki/Q5438159","display_name":"Fault indicator","level":4,"score":0.2964000105857849},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29409998655319214},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C2776365744","wikidata":"https://www.wikidata.org/wiki/Q5438149","display_name":"Fault Simulator","level":5,"score":0.289900004863739},{"id":"https://openalex.org/C32211213","wikidata":"https://www.wikidata.org/wiki/Q47083","display_name":"Ohm","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.array.2026.101024","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.101024","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:41b6197579894503bc697c8ecb47c05e","is_oa":false,"landing_page_url":"https://doaj.org/article/41b6197579894503bc697c8ecb47c05e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Array, Vol 31, Iss , Pp 101024- (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.array.2026.101024","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.101024","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.44394975900650024}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2542145506","https://openalex.org/W2897890915","https://openalex.org/W3005557219","https://openalex.org/W3091832488","https://openalex.org/W3133082861","https://openalex.org/W3158366413","https://openalex.org/W3200759122","https://openalex.org/W4210719279","https://openalex.org/W4285731897","https://openalex.org/W4303700060","https://openalex.org/W4309198707","https://openalex.org/W4309224966","https://openalex.org/W4321020941","https://openalex.org/W4322500397","https://openalex.org/W4380785740","https://openalex.org/W4385949007","https://openalex.org/W4386492778","https://openalex.org/W4387667797","https://openalex.org/W4388328090","https://openalex.org/W4388742887","https://openalex.org/W4390077933","https://openalex.org/W4394011484","https://openalex.org/W4404080573","https://openalex.org/W4404484025","https://openalex.org/W4408400356","https://openalex.org/W4410428537","https://openalex.org/W4417123591","https://openalex.org/W7119012896"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,29,137,189],"exhaustive":[4],"machine":[5,96],"learning":[6,97],"model":[7,25],"for":[8,161,183,232,245],"fault":[9,68,71,162,209,234],"resistance":[10],"estimation":[11],"and":[12,44,82,87,106,114,119,136,174,194,226,250],"classification":[13,120],"in":[14,185,247],"11kV":[15],"distribution":[16],"networks.":[17],"In":[18,150],"MATLAB/Simulink,":[19],"a":[20,41,45,62,127,143,239],"high-fidelity":[21],"Simscape":[22],"Power":[23],"Systems":[24],"is":[26,58,180],"built":[27],"around":[28],"11kV,":[30],"30":[31],"MVA":[32],"source":[33],"connected":[34],"to":[35,60,77,207],"two":[36],"100km":[37],"PI-section":[38],"transmission":[39],"lines,":[40],"midpoint-fault":[42],"bus,":[43],"downstream":[46],"1":[47],"MVA,":[48],"11kV/0.4kV":[49],"transformer\u2014Monte":[50],"Carlo":[51,56],"simulation.":[52],"An":[53],"automated":[54],"Monte":[55],"simulation":[57],"used":[59],"generate":[61],"dataset":[63],"of":[64,133,140,147,191],"5000":[65],"Phase-A":[66],"single-line-to-ground":[67],"scenarios,":[69],"with":[70,188],"resistances":[72],"uniformly":[73],"distributed":[74],"from":[75],"0.01":[76],"50":[78,175,202],"ohm.":[79],"Three-phase":[80],"voltage":[81],"current":[83],"waveforms":[84],"are":[85,112],"extracted":[86],"converted":[88],"into":[89],"20":[90],"physical,":[91],"interpretable":[92],"time-domain":[93],"features.":[94],"Three":[95],"models":[98],"\u2014":[99,111],"Gradient":[100,123],"Boosting":[101,124],"(GB),":[102],"Random":[103,153],"Forest":[104,154],"(RF),":[105],"Support":[107],"Vector":[108],"Machine":[109],"(SVM)":[110],"trained":[113],"benchmarked":[115],"on":[116,142],"both":[117],"regression":[118,228],"tasks.":[121],"The":[122,165],"regressor":[125],"achieves":[126,156],"root":[128],"mean":[129],"square":[130],"error":[131],"(RMSE)":[132],"0.0276":[134],"ohm":[135,193],"R":[138],"2":[139],"1.0000":[141],"held-out":[144],"test":[145],"set":[146],"1000":[148],"samples.":[149],"contrast,":[151],"the":[152],"classifier":[155],"99.90":[157],"per":[158,196],"cent":[159,197],"accuracy":[160,198,225],"severity":[163],"classification.":[164],"noise-robustness":[166],"analysis":[167],"at":[168,199],"signal-to-noise":[169],"ratios":[170],"(SNRs)":[171],"between":[172],"10":[173],"dB":[176],"shows":[177],"that":[178],"RF":[179],"more":[181],"favourable":[182],"use":[184],"noisy":[186],"environments,":[187],"RMSE":[190],"0.96":[192],"96.72":[195],"SNR":[200],"=":[201],"dB.":[203],"A":[204],"multi-fault-type":[205],"extension":[206],"seven":[208],"configurations":[210],"(AG,":[211],"BG,":[212],"CG,":[213],"ABG,":[214],"BCG,":[215],"AB,":[216],"ABCG)":[217],"over":[218],"3500":[219],"simulations":[220],"demonstrates":[221],"100%":[222],"fault-type":[223],"identification":[224],"per-type":[227],"R-squared":[229],"exceeding":[230],"0.996":[231],"all":[233],"types.":[235],"These":[236],"results":[237],"establish":[238],"robust,":[240],"simulation-driven":[241],"machine-learning":[242],"pipeline":[243],"suitable":[244],"deployment":[246],"adaptive":[248],"protection":[249],"fault-management":[251],"systems.":[252]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-06-19T00:00:00"}
