{"id":"https://openalex.org/W3085697539","doi":"https://doi.org/10.23919/fusion45008.2020.9190216","title":"A Method for Dissolved Gas Forecasting in Power Transformers Using LS-SVM","display_name":"A Method for Dissolved Gas Forecasting in Power Transformers Using LS-SVM","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3085697539","doi":"https://doi.org/10.23919/fusion45008.2020.9190216","mag":"3085697539"},"language":"en","primary_location":{"id":"doi:10.23919/fusion45008.2020.9190216","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","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/A5025605490","display_name":"J. M. Atherfold","orcid":null},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":true,"raw_author_name":"J Atherfold","raw_affiliation_strings":["School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044690353","display_name":"Terence L. van Zyl","orcid":"https://orcid.org/0000-0003-4281-630X"},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"T. L Van Zyl","raw_affiliation_strings":["School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025605490"],"corresponding_institution_ids":["https://openalex.org/I192619145"],"apc_list":null,"apc_paid":null,"fwci":0.6165,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.6796619,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9994999766349792,"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/T11343","display_name":"Power Transformer Diagnostics and Insulation","score":0.9994999766349792,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9850000143051147,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9710999727249146,"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/exponential-smoothing","display_name":"Exponential smoothing","score":0.7470116019248962},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7144730091094971},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5712429881095886},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5537523031234741},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5358572602272034},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5203777551651001},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5092839002609253},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5034372210502625},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.49750617146492004},{"id":"https://openalex.org/keywords/dissolved-gas-analysis","display_name":"Dissolved gas analysis","score":0.45062118768692017},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4347195327281952},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.4301522672176361},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.42711132764816284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38496071100234985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35651230812072754},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2767636477947235},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.16690972447395325},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.14578306674957275}],"concepts":[{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.7470116019248962},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7144730091094971},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5712429881095886},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5537523031234741},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5358572602272034},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5203777551651001},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5092839002609253},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5034372210502625},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.49750617146492004},{"id":"https://openalex.org/C81818771","wikidata":"https://www.wikidata.org/wiki/Q787515","display_name":"Dissolved gas analysis","level":5,"score":0.45062118768692017},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4347195327281952},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.4301522672176361},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.42711132764816284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38496071100234985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35651230812072754},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2767636477947235},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.16690972447395325},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.14578306674957275},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C181335627","wikidata":"https://www.wikidata.org/wiki/Q901418","display_name":"Transformer oil","level":4,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/fusion45008.2020.9190216","is_oa":false,"landing_page_url":"https://doi.org/10.23919/fusion45008.2020.9190216","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Information Fusion (FUSION)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1663973292","https://openalex.org/W1964484746","https://openalex.org/W1982576552","https://openalex.org/W1990011917","https://openalex.org/W2042816221","https://openalex.org/W2058952413","https://openalex.org/W2074128934","https://openalex.org/W2074910498","https://openalex.org/W2078980147","https://openalex.org/W2101234009","https://openalex.org/W2107116457","https://openalex.org/W2123967948","https://openalex.org/W2145487113","https://openalex.org/W2170559427","https://openalex.org/W2181523240","https://openalex.org/W2317175826","https://openalex.org/W2548153325","https://openalex.org/W2563914516","https://openalex.org/W2740545405","https://openalex.org/W2787894218","https://openalex.org/W4212863985","https://openalex.org/W4239414278","https://openalex.org/W4301501800","https://openalex.org/W6675354045","https://openalex.org/W6729339441","https://openalex.org/W6742086817"],"related_works":["https://openalex.org/W4387220233","https://openalex.org/W2955496313","https://openalex.org/W4220924527","https://openalex.org/W3097024643","https://openalex.org/W3215481666","https://openalex.org/W4210402713","https://openalex.org/W4220732081","https://openalex.org/W2024558842","https://openalex.org/W3124446684","https://openalex.org/W2145732764"],"abstract_inverted_index":{"Maintenance":[0],"data":[1,66,72,87,108,235],"from":[2,101],"power":[3,27,45],"transformers":[4,28,227],"are":[5],"typically":[6],"in":[7,163,197],"the":[8,24,39,83,102,107,165,181,191,198,206,209,222,229,234],"form":[9],"of":[10,26,41,44,49,57,71,85,157,208],"dissolved":[11],"gas":[12],"analysis":[13],"time":[14,30,238],"series":[15,31],"data.":[16],"This":[17],"research":[18],"attempts":[19],"consolidating":[20],"industry":[21,95,192],"knowledge":[22],"on":[23],"maintenance":[25,43],"and":[29,63,152,168,185],"forecasting":[32,50,122],"techniques":[33,123],"into":[34],"a":[35,135],"coherent":[36],"system":[37],"for":[38,82,98,177],"purpose":[40],"predictive":[42],"transformers.":[46],"The":[47,121],"generalisability":[48,215],"models":[51,59,223,231],"is":[52,74],"investigated":[53],"by":[54],"measuring":[55],"performance":[56],"single":[58],"across":[60,225],"multiple":[61,65],"transformers,":[62],"hence,":[64],"sets.":[67],"A":[68],"novel":[69],"method":[70],"preprocessing":[73],"utilized;":[75],"an":[76,186],"exponential":[77],"smoothing":[78],"technique":[79],"specifically":[80],"designed":[81],"type":[84],"raw":[86],"received":[88],"(aperiodically":[89],"sampled,":[90],"noisy":[91],"data).":[92],"In":[93],"addition,":[94],"specified":[96,193],"features":[97,112],"fault":[99],"classification":[100],"literature":[103],"were":[104,113,159,171,175,212],"added":[105],"to":[106,115],"set.":[109],"These":[110,173],"other":[111,203],"examined":[114],"see":[116],"if":[117],"they":[118],"improved":[119],"forecasting.":[120],"evaluated":[124],"included":[125],"Least-Squares":[126],"Support":[127,139],"Vector":[128,140],"Machine":[129],"(LS-SVM)":[130],"with":[131,190],"hyper-parameters":[132],"optimized":[133],"using":[134],"Particle":[136],"Swarm":[137],"Optimisation;":[138],"Regressors;":[141],"Naive":[142],"forecasts;":[143,145],"Mean":[144],"Auto-Regressive":[146],"Integrated":[147],"Moving":[148],"average":[149],"or":[150],"ARIMA;":[151],"Exponential":[153],"Smoothing.":[154],"Two":[155],"sets":[156,170],"experiments":[158,174],"run,":[160],"which":[161],"differed":[162],"how":[164],"Training,":[166],"Validation,":[167],"Testing":[169,210],"chosen.":[172],"run":[176],"different":[178],"input":[179,183,187],"vectors;":[180],"original":[182],"vector":[184,188],"augmented":[189],"features.":[194],"Models":[195],"trained":[196,224],"second":[199],"experiment":[200],"outperformed":[201,228],"all":[202,226],"models,":[204],"when":[205],"distributions":[207],"errors":[211],"considered.":[213],"When":[214],"was":[216,219],"considered,":[217],"it":[218],"found":[220],"that":[221,232],"per-transformer":[230],"treated":[233],"as":[236],"univariate":[237],"series.":[239]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
