{"refrec":{"BRefID":382919,"RR":"<b>Chesterman, X.; Verstraeten, T.; Daems, P.J.; Nowé, A.; Helsen, J.</b> (2023). Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms. <i>Wind Energy Science 8(6)</i>: 893-924. <a href=\"https://dx.doi.org/10.5194/wes-8-893-2023\" target=\"_blank\">https://dx.doi.org/10.5194/wes-8-893-2023</a>","BEntID":380661,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Wind Energy Science 8(6)</i>: 893-924. <a href=\"https://dx.doi.org/10.5194/wes-8-893-2023\" target=\"_blank\">https://dx.doi.org/10.5194/wes-8-893-2023</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Chesterman, X.; Verstraeten, T.; Daems, P.J.; Nowé, A.; Helsen, J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Chesterman, X. <i>et al.</i>","Englishabstract":"Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g.,&nbsp;a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2024-12-10 01:33:17.368041","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2024-02-19","DateCreate":"2024-02-16","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001000433400001","VABBcode":null,"OpenAcc":1,"DOI":"10.5194/wes-8-893-2023"},"refs":null,"anarec":{"AnaID":382919,"PubliDate":2023,"Pagination":"893-924","XtraPublOfAnaID":null,"ISBN":null,"Volume":"8","Issue":"6","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":311972,"SerRR":"Wind Energy Science. 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ISSN 2366-7443; e-ISSN 2366-7451","StandardTitleSer":"Wind Energy Science","ISSN":"2366-7443","AbbrevSer":null,"StandardTitleMon":null,"StartPage":893,"Pages":32,"ToPubliDate":null,"BRefBibLvlCode":"S","SerNotes":null},"monrec":null,"serrec":null,"relations":null,"relationsRev":null,"addrec":null,"othpubs":null,"ownerships":null,"authors":[{"AutName":"Chesterman","Firstname":"Xavier","Initials":"X.","Affiliation":"Artificial Intelligence Lab, Vrije Universiteit Brussel","Discriminator":null,"CorporateFlag":0,"BEntID":380661,"AutID":555774,"OrderNr":1,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":"Vrije Universiteit Brussel; Faculteit Wetenschappen & Bio-ingenieurswetenschappen; Vakgroep Computerwetenschappen; Artificial Intelligence Laboratory","ORCID":"0000-0001-8348-0019","PersID":44515,"InsID":13971},{"AutName":"Verstraeten","Firstname":"Timothy","Initials":"T.","Affiliation":"Artificial Intelligence Lab, Vrije Universiteit Brussel","Discriminator":null,"CorporateFlag":0,"BEntID":380661,"AutID":555775,"OrderNr":2,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":"Vrije Universiteit Brussel; 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