{"id":"https://openalex.org/W2729807182","doi":"https://doi.org/10.1109/siu.2017.7960216","title":"Classification of power quality disturbances with S-transform and artificial neural networks method","display_name":"Classification of power quality disturbances with S-transform and artificial neural networks method","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2729807182","doi":"https://doi.org/10.1109/siu.2017.7960216","mag":"2729807182"},"language":"en","primary_location":{"id":"doi:10.1109/siu.2017.7960216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2017.7960216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/20.500.12628/23875","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008405882","display_name":"Se\u00e7kin Karasu","orcid":"https://orcid.org/0000-0001-5277-5252"},"institutions":[{"id":"https://openalex.org/I3017902709","display_name":"B\u00fclent Ecevit University","ror":"https://ror.org/01dvabv26","country_code":"TR","type":"education","lineage":["https://openalex.org/I3017902709"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Seckin Karasu","raw_affiliation_strings":["Elektrik Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, B\u00fclent Ecevit \u00dcniversitesi, Zonguldak, T\u00fcrkiye"],"affiliations":[{"raw_affiliation_string":"Elektrik Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, B\u00fclent Ecevit \u00dcniversitesi, Zonguldak, T\u00fcrkiye","institution_ids":["https://openalex.org/I3017902709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008877768","display_name":"Zehra Sara\u00e7","orcid":"https://orcid.org/0000-0003-3330-5196"},"institutions":[{"id":"https://openalex.org/I3017902709","display_name":"B\u00fclent Ecevit University","ror":"https://ror.org/01dvabv26","country_code":"TR","type":"education","lineage":["https://openalex.org/I3017902709"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Zehra Sarac","raw_affiliation_strings":["Elektrik Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, B\u00fclent Ecevit \u00dcniversitesi, Zonguldak, T\u00fcrkiye"],"affiliations":[{"raw_affiliation_string":"Elektrik Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, B\u00fclent Ecevit \u00dcniversitesi, Zonguldak, T\u00fcrkiye","institution_ids":["https://openalex.org/I3017902709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008405882"],"corresponding_institution_ids":["https://openalex.org/I3017902709"],"apc_list":null,"apc_paid":null,"fwci":0.872,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75817558,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10573","display_name":"Power Quality and Harmonics","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10573","display_name":"Power Quality and Harmonics","score":0.9998999834060669,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9696000218391418,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.8106405735015869},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6680432558059692},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6139087677001953},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.593987226486206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5683811902999878},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5678881406784058},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.45038512349128723},{"id":"https://openalex.org/keywords/artificial-neuron","display_name":"Artificial neuron","score":0.4441757798194885},{"id":"https://openalex.org/keywords/power-quality","display_name":"Power quality","score":0.42930203676223755},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.3916756510734558},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38690099120140076}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.8106405735015869},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6680432558059692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6139087677001953},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.593987226486206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5683811902999878},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5678881406784058},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.45038512349128723},{"id":"https://openalex.org/C2776990819","wikidata":"https://www.wikidata.org/wiki/Q177058","display_name":"Artificial neuron","level":3,"score":0.4441757798194885},{"id":"https://openalex.org/C2779665505","wikidata":"https://www.wikidata.org/wiki/Q1780079","display_name":"Power quality","level":3,"score":0.42930203676223755},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3916756510734558},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38690099120140076},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/siu.2017.7960216","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2017.7960216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"},{"id":"pmh:oai:acikarsiv.beun.edu.tr:20.500.12628/23875","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.12628/23875","pdf_url":null,"source":{"id":"https://openalex.org/S7407055168","display_name":"Zonguldak B\u00fclent Ecevit University Institutional Repository","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:acikarsiv.beun.edu.tr:20.500.12628/23875","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.12628/23875","pdf_url":null,"source":{"id":"https://openalex.org/S7407055168","display_name":"Zonguldak B\u00fclent Ecevit University Institutional Repository","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W759582212","https://openalex.org/W1978718488","https://openalex.org/W2002674327","https://openalex.org/W2012315090","https://openalex.org/W2057163688","https://openalex.org/W2080045616","https://openalex.org/W2150890277","https://openalex.org/W2426710423","https://openalex.org/W2477899005","https://openalex.org/W2482589566","https://openalex.org/W6717746057"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2391251536","https://openalex.org/W2468698815","https://openalex.org/W3035049005","https://openalex.org/W2162051485"],"abstract_inverted_index":{"In":[0,91],"this":[1,92],"study,":[2,93],"classification":[3,34],"of":[4,44,66,103],"11":[5],"different":[6,69,76],"Power":[7],"Quality":[8],"(PQ)":[9],"disturbances":[10],"with":[11,24,68],"Artificial":[12],"Neural":[13],"Networks":[14],"(ANN)":[15],"has":[16],"been":[17],"done":[18],"by":[19,39,58,86],"using":[20,40,87],"the":[21,41,54,88,99],"attributes":[22,45,55,104],"obtained":[23],"S-Transform.":[25],"It":[26],"was":[27,95],"aimed":[28],"to":[29],"achieve":[30],"accurate":[31],"and":[32,83,105,115],"high":[33],"performance":[35,65,109,117],"in":[36,110],"noisy":[37,111],"environment":[38,112],"least":[42],"number":[43,102],"representing":[46],"PQ":[47],"disturbances.":[48],"The":[49,64],"most":[50,100],"suitable":[51],"ones":[52],"from":[53],"were":[56,118],"selected":[57,89],"Sequential":[59],"Forward":[60],"Selection":[61],"(SFS)":[62],"method.":[63],"models":[67],"hidden":[70],"layer":[71],"neuron":[72],"numbers":[73],"tested":[74],"at":[75],"noise":[77],"levels":[78],"(40":[79],"dB,":[80],"30":[81],"dB":[82],"20":[84],"dB)":[85,114],"attributes.":[90],"it":[94],"found":[96],"that":[97],"for":[98],"appropriate":[101],"optimal":[106],"model":[107],"parameters,":[108],"(20":[113],"overall":[116],"99.0%.":[119]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}
