{"id":"https://openalex.org/W4386323950","doi":"https://doi.org/10.1109/isie51358.2023.10227977","title":"Power Quality Disturbance Identification Algorithm Based on Empirical Wavelet Transform And Time-Domain Kurtosis Feature Analysis","display_name":"Power Quality Disturbance Identification Algorithm Based on Empirical Wavelet Transform And Time-Domain Kurtosis Feature Analysis","publication_year":2023,"publication_date":"2023-06-19","ids":{"openalex":"https://openalex.org/W4386323950","doi":"https://doi.org/10.1109/isie51358.2023.10227977"},"language":"en","primary_location":{"id":"doi:10.1109/isie51358.2023.10227977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie51358.2023.10227977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100458219","display_name":"Chi Zhang","orcid":"https://orcid.org/0000-0003-0034-843X"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chi Zhang","raw_affiliation_strings":["Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","institution_ids":["https://openalex.org/I4210126065"]},{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104259889","display_name":"Yizhi Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yizhi Zhu","raw_affiliation_strings":["Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","institution_ids":["https://openalex.org/I4210126065"]},{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026439520","display_name":"Caiyang Yu","orcid":"https://orcid.org/0000-0001-8246-1561"},"institutions":[{"id":"https://openalex.org/I4210126065","display_name":"Shanghai Electric (China)","ror":"https://ror.org/0314qy595","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caiyang Yu","raw_affiliation_strings":["Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd,Zhejiang,China","institution_ids":["https://openalex.org/I4210126065"]},{"raw_affiliation_string":"Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd, Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101069031","display_name":"Jiawei Bao","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Bao","raw_affiliation_strings":["Southeast University,School of Electrical Engineering,Nanjing,China,210096"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Electrical Engineering,Nanjing,China,210096","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061332500","display_name":"Qingsong Wang","orcid":"https://orcid.org/0000-0002-0066-9973"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingsong Wang","raw_affiliation_strings":["Southeast University,School of Electrical Engineering,Nanjing,China,210096"],"affiliations":[{"raw_affiliation_string":"Southeast University,School of Electrical Engineering,Nanjing,China,210096","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085411451","display_name":"Giuseppe Buja","orcid":"https://orcid.org/0000-0003-4245-1742"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giuseppe Buja","raw_affiliation_strings":["University of Padova,Department of Industrial Engineering,Padova,Italy,35131"],"affiliations":[{"raw_affiliation_string":"University of Padova,Department of Industrial Engineering,Padova,Italy,35131","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100458219"],"corresponding_institution_ids":["https://openalex.org/I4210126065"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03856163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13955","display_name":"Evaluation Methods in Various Fields","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10573","display_name":"Power Quality and Harmonics","score":0.9801999926567078,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9609000086784363,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.723904550075531},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6955008506774902},{"id":"https://openalex.org/keywords/kurtosis","display_name":"Kurtosis","score":0.6820292472839355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6806949973106384},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6583812236785889},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.554640531539917},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5411846041679382},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5159614682197571},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5136429667472839},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5073170065879822},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4427955746650696},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.40911567211151123},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32067131996154785},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17705145478248596},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13478612899780273}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.723904550075531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6955008506774902},{"id":"https://openalex.org/C166963901","wikidata":"https://www.wikidata.org/wiki/Q287251","display_name":"Kurtosis","level":2,"score":0.6820292472839355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6806949973106384},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6583812236785889},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.554640531539917},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5411846041679382},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5159614682197571},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5136429667472839},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5073170065879822},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4427955746650696},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.40911567211151123},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32067131996154785},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17705145478248596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13478612899780273},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isie51358.2023.10227977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isie51358.2023.10227977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.9100000262260437,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320326707","display_name":"State Grid Corporation of China","ror":"https://ror.org/05twwhs70"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1974028429","https://openalex.org/W2023519762","https://openalex.org/W2778770026","https://openalex.org/W2899089809","https://openalex.org/W2909189866","https://openalex.org/W2973247625","https://openalex.org/W4205321420","https://openalex.org/W4249840798"],"related_works":["https://openalex.org/W4381516319","https://openalex.org/W2037499216","https://openalex.org/W1506384729","https://openalex.org/W4225568567","https://openalex.org/W4286378979","https://openalex.org/W3127045225","https://openalex.org/W2075698830","https://openalex.org/W3216026256","https://openalex.org/W2059891554","https://openalex.org/W2027857183"],"abstract_inverted_index":{"In":[0],"the":[1,16,37,70,76,88,94,100,106,112,117,123,129,134,145,155,168,187,198,206],"process":[2],"of":[3,15,26,43,105,147,167,180,190,201],"power":[4,208],"quality":[5,209],"(PQ)":[6],"disturbance":[7,17,148,210],"recognition,":[8],"there":[9],"is":[10,62,66,120,138,183],"redundancy":[11,83],"in":[12],"feature":[13,56,108,162],"extraction":[14],"signal,":[18],"so":[19,96],"it":[20],"will":[21],"lead":[22],"to":[23,68,81,87,98,110,127,143],"complex":[24],"structure":[25],"recognition":[27,33,44,141,170,178,188,199,211],"model,":[28,171],"difficult":[29],"training":[30,173],"and":[31,45,58,92,102,175,194],"low":[32],"accuracy.":[34],"To":[35],"address":[36],"above":[38],"problems,":[39],"a":[40],"new":[41],"method":[42,159],"classification":[46],"based":[47],"on":[48],"empirical":[49],"wavelet":[50],"transform":[51],"(EWT)":[52],"with":[53,205],"time-domain":[54,90],"kurtosis":[55,95],"analysis":[57,72,142],"LSTM":[59,135,169],"neural":[60,114,136,192],"network":[61,115,137],"proposed.":[63],"Firstly,":[64],"EWT":[65],"used":[67,139],"obtain":[69,128],"multi-resolution":[71],"(MRA)":[73],"components;":[74],"secondly,":[75],"MRA":[77,125],"segments":[78,126],"are":[79],"filtered":[80,124],"reduce":[82],"by":[84],"adding":[85],"windows":[86],"decomposed":[89],"signals":[91],"calculating":[93],"as":[97],"determine":[99],"starting":[101],"ending":[103],"positions":[104],"effective":[107],"information;":[109],"facilitate":[111],"subsequent":[113],"training,":[116],"energy":[118,130],"entropy":[119,131],"calculated":[121],"for":[122,140],"sequence.":[132],"Finally,":[133],"identify":[144],"types":[146],"signals.":[149],"The":[150],"simulation":[151],"results":[152,189,200],"show":[153],"that":[154],"proposed":[156],"optimized":[157],"screening":[158],"has":[160],"lower":[161,165],"vector":[163,202],"redundancy,":[164],"complexity":[166],"shorter":[172],"time,":[174],"an":[176],"average":[177],"accuracy":[179],"98.22%,":[181],"which":[182],"8.04%":[184],"better":[185,196],"than":[186,197],"traditional":[191,207],"networks":[193],"11.04%":[195],"machines,":[203],"compared":[204],"method.":[212]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
