{"id":"https://openalex.org/W7204032668","doi":"https://doi.org/10.37394/23209.2027.24.5","title":"Intelligent Fault Identification in Power Systems Via Wavelet Transform and Machine Learning: A Comparative Study of Four Classifiers","display_name":"Intelligent Fault Identification in Power Systems Via Wavelet Transform and Machine Learning: A Comparative Study of Four Classifiers","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7204032668","doi":"https://doi.org/10.37394/23209.2027.24.5"},"language":"en","primary_location":{"id":"doi:10.37394/23209.2027.24.5","doi":"https://doi.org/10.37394/23209.2027.24.5","is_oa":true,"landing_page_url":"https://doi.org/10.37394/23209.2027.24.5","pdf_url":"https://wseas.com/journals/isa/2027/a105109-1280.pdf","source":{"id":"https://openalex.org/S2764602731","display_name":"WSEAS Transactions on Information Science and Applications archive","issn_l":"1790-0832","issn":["1790-0832","2224-3402"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-20","russia-white-list-4"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"WSEAS Transactions on Information Science and Applications","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://wseas.com/journals/isa/2027/a105109-1280.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147882039","display_name":"Radwan Awayda","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I168913471","display_name":"University of Balamand","ror":"https://ror.org/01xvwxv41","country_code":"LB","type":"education","lineage":["https://openalex.org/I168913471"]}],"countries":["LB"],"is_corresponding":false,"raw_author_name":"Radwan M. Awayda","raw_affiliation_strings":["Department of Electrical Engineering University of Balamand Kalhat, Al-Kurah, P.O. box 100, Tripoli, LEBANON"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering University of Balamand Kalhat, Al-Kurah, P.O. box 100, Tripoli, LEBANON","institution_ids":["https://openalex.org/I168913471"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023084996","display_name":"Jihad Daba","orcid":"https://orcid.org/0000-0001-9093-5296","observed_orcids":["https://orcid.org/0000-0001-9093-5296"]},"institutions":[{"id":"https://openalex.org/I168913471","display_name":"University of Balamand","ror":"https://ror.org/01xvwxv41","country_code":"LB","type":"education","lineage":["https://openalex.org/I168913471"]}],"countries":["LB"],"is_corresponding":false,"raw_author_name":"Jihad S. Daba","raw_affiliation_strings":["Department of Electrical Engineering University of Balamand Kalhat, Al-Kurah, P.O. box 100, Tripoli, LEBANON"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering University of Balamand Kalhat, Al-Kurah, P.O. box 100, Tripoli, LEBANON","institution_ids":["https://openalex.org/I168913471"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168913471"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":null,"first_page":"41","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10972","display_name":"Power Systems Fault Detection","score":0.9994999766349792,"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":0.9994999766349792,"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":0.0003000000142492354,"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/T10305","display_name":"Power System Optimization and Stability","score":9.999999747378752e-05,"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/fault-identification","display_name":"fault identification","score":0.8339999914169312},{"id":"https://openalex.org/keywords/power-system-protection","display_name":"power system protection","score":0.9559999704360962},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"discrete wavelet transform","score":0.9890000224113464},{"id":"https://openalex.org/keywords/fault-current-signals","display_name":"fault current signals","score":0.6759999990463257},{"id":"https://openalex.org/keywords/classifier-comparison","display_name":"classifier comparison","score":0.7110000252723694},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8999999761581421},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9890000224113464},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9879999756813049},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9919999837875366},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.5490000247955322},{"id":"https://openalex.org/keywords/ieee-9-bus-system","display_name":"IEEE 9-bus system","score":0.781000018119812},{"id":"https://openalex.org/keywords/power-system-fault-diagnosis","display_name":"power system fault diagnosis","score":0.6230000257492065}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845000147819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5688999891281128},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5142999887466431},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.45879998803138733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4553999900817871},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296000063419342},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3086000084877014},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28040000796318054},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.2567000091075897},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.37394/23209.2027.24.5","doi":"https://doi.org/10.37394/23209.2027.24.5","is_oa":true,"landing_page_url":"https://doi.org/10.37394/23209.2027.24.5","pdf_url":"https://wseas.com/journals/isa/2027/a105109-1280.pdf","source":{"id":"https://openalex.org/S2764602731","display_name":"WSEAS Transactions on Information Science and Applications archive","issn_l":"1790-0832","issn":["1790-0832","2224-3402"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-20","russia-white-list-4"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"WSEAS Transactions on Information Science and Applications","raw_type":"journal-article"},{"id":"doi:10.13140/rg.2.2.20877.70889","doi":"https://doi.org/10.13140/rg.2.2.20877.70889","is_oa":false,"landing_page_url":"https://doi.org/10.13140/rg.2.2.20877.70889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.37394/23209.2027.24.5","doi":"https://doi.org/10.37394/23209.2027.24.5","is_oa":true,"landing_page_url":"https://doi.org/10.37394/23209.2027.24.5","pdf_url":"https://wseas.com/journals/isa/2027/a105109-1280.pdf","source":{"id":"https://openalex.org/S2764602731","display_name":"WSEAS Transactions on Information Science and Applications archive","issn_l":"1790-0832","issn":["1790-0832","2224-3402"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","poland-20","russia-white-list-4"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"WSEAS Transactions on Information Science and Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.7207000255584717},{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.4000000059604645}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204032668.pdf","grobid_xml":"https://content.openalex.org/works/W7204032668.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1973582484","https://openalex.org/W4301021124","https://openalex.org/W4306394625","https://openalex.org/W4385284089","https://openalex.org/W4399549149","https://openalex.org/W4402782447","https://openalex.org/W4405390939","https://openalex.org/W4406063176","https://openalex.org/W4406323848","https://openalex.org/W4410354864","https://openalex.org/W4410734712","https://openalex.org/W4414110725","https://openalex.org/W4414445390","https://openalex.org/W4414713385","https://openalex.org/W7082258233","https://openalex.org/W7124466171","https://openalex.org/W7166538314"],"related_works":["https://openalex.org/W6960154321","https://openalex.org/W2502542167","https://openalex.org/W4238390987","https://openalex.org/W6884971516","https://openalex.org/W2548358407","https://openalex.org/W846627835","https://openalex.org/W6960144687","https://openalex.org/W3196032337","https://openalex.org/W2519786324","https://openalex.org/W1479839354"],"abstract_inverted_index":{"Accurate":[0],"fault":[1,15,23,48,57,167,190],"identification":[2,24],"remains":[3],"a":[4,104,173],"critical":[5],"challenge":[6],"in":[7,44,192],"modern":[8],"power":[9,40,193],"system":[10,41],"protection,":[11],"particularly":[12],"under":[13],"varying":[14],"impedance":[16],"conditions.":[17],"This":[18],"paper":[19],"proposes":[20],"an":[21,185],"intelligent":[22,189],"framework":[25,183],"that":[26,144],"combines":[27],"the":[28,72,84,90,157,162],"Discrete":[29],"Wavelet":[30],"Transform":[31],"(DWT)":[32],"with":[33,56,83],"machine":[34],"learning":[35],"classifiers.":[36],"The":[37,154,181],"IEEE":[38],"9-bus":[39],"is":[42,130],"modeled":[43],"MATLAB/Simulink,":[45],"where":[46],"twelve":[47],"types":[49],"are":[50,69,79,100,114],"applied":[51],"at":[52],"two":[53],"bus":[54],"locations":[55],"impedances":[58],"ranging":[59],"from":[60,108],"0.0001":[61],"\u03a9":[62],"to":[63,102,165],"10":[64],"\u03a9.":[65],"Pre-fault":[66],"operating":[67],"conditions":[68],"established":[70],"using":[71,81,132],"Newton\u2013Raphson":[73],"load-flow":[74],"method.":[75],"Fault":[76],"current":[77],"signals":[78],"processed":[80],"DWT":[82],"Daubechies":[85],"(db4)":[86],"mother":[87],"wavelet,":[88],"and":[89,97,124,139,161,178],"maximum":[91],"absolute":[92],"detail":[93],"coefficients":[94],"of":[95],"three-phase":[96],"ground":[98],"currents":[99],"extracted":[101],"form":[103],"four-dimensional":[105],"feature":[106],"vector":[107],"2400":[109],"simulation":[110],"samples.":[111],"Four":[112],"classifiers":[113,146],"evaluated:":[115],"Logistic":[116],"Regression,":[117],"Support":[118],"Vector":[119],"Machine":[120],"(SVM),":[121],"Random":[122,170],"Forest,":[123],"Artificial":[125],"Neural":[126],"Network":[127],"(ANN).":[128],"Performance":[129],"assessed":[131],"classification":[133],"accuracy,":[134],"confusion":[135],"matrices,":[136],"ROC":[137],"analysis,":[138],"computational":[140,179],"efficiency.":[141],"Results":[142],"show":[143],"non-linear":[145],"outperform":[147],"linear":[148],"approaches,":[149],"achieving":[150],"accuracies":[151],"above":[152],"95%.":[153],"ANN":[155],"attains":[156],"highest":[158],"accuracy":[159,177],"(98.3%)":[160],"greatest":[163],"robustness":[164],"increasing":[166],"impedance,":[168],"while":[169],"Forest":[171],"provides":[172],"favorable":[174],"balance":[175],"between":[176],"cost.":[180],"proposed":[182],"offers":[184],"effective":[186],"solution":[187],"for":[188],"diagnosis":[191],"systems.":[194]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-08-24T00:00:00"}