Advanced analytics for detection and diagnosis of false alarms and faults: A real case study
Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful informa...
Gespeichert in:
| Veröffentlicht in: | Wind energy (Chichester, England) England), 2019-11, Vol.22 (11), p.1622-1635 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1635 |
|---|---|
| container_issue | 11 |
| container_start_page | 1622 |
| container_title | Wind energy (Chichester, England) |
| container_volume | 22 |
| creator | Pliego Marugán, Alberto García Márquez, Fausto Pedro |
| description | Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms. |
| doi_str_mv | 10.1002/we.2393 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2307645042</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2307645042</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3703-8b8b21871496e677d7eab073c8a9242aba3c1e8568ed8896718c35234c909ef3</originalsourceid><addsrcrecordid>eNp10E1Lw0AQBuBFFKxV_AsLHjxI6n4lu-utlPoBBS8FL8Ky2Z1ISprU3cSQf2-aevU0w7wPAzMI3VKyoISwxx4WjGt-hmaUaJ1QxcT51KeJYEJcoqsYd4RQQqmaoc-l_7G1A49tbauhLV3ERROwhxZcWzb1OPfYl_arbmIZcVPgwlYRsK1s2McpLWxXtfEJL3EAW2Fnxzi2nR-u0cWEb_7qHG2f19vVa7J5f3lbLTeJ45LwROUqZ1RJKnQGmZRegs2J5E5ZzQSzueWOgkozBV4pnUmqHE8ZF04TDQWfo7vT2kNovjuIrdk1XRjPiYZxIjOREsFGdX9SLjQxBijMIZR7GwZDiTl-zvRgjp8b5cNJ9mUFw3_MfKwn_QtcGWzp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2307645042</pqid></control><display><type>article</type><title>Advanced analytics for detection and diagnosis of false alarms and faults: A real case study</title><source>Access via Wiley Online Library</source><creator>Pliego Marugán, Alberto ; García Márquez, Fausto Pedro</creator><creatorcontrib>Pliego Marugán, Alberto ; García Márquez, Fausto Pedro</creatorcontrib><description>Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.</description><identifier>ISSN: 1095-4244</identifier><identifier>EISSN: 1099-1824</identifier><identifier>DOI: 10.1002/we.2393</identifier><language>eng</language><publisher>Bognor Regis: John Wiley & Sons, Inc</publisher><subject>Alarms ; analytics ; Case studies ; Condition monitoring ; Control systems ; Correlation analysis ; Curve fitting ; Data acquisition ; False alarms ; Fault detection ; Fault diagnosis ; Maintenance ; multivariable analysis ; Offshore energy sources ; Offshore operations ; Pattern recognition ; reliability ; SCADA ; Supervisory control and data acquisition ; Turbines ; Wind farms ; Wind power ; wind turbine ; Wind turbines</subject><ispartof>Wind energy (Chichester, England), 2019-11, Vol.22 (11), p.1622-1635</ispartof><rights>2019 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3703-8b8b21871496e677d7eab073c8a9242aba3c1e8568ed8896718c35234c909ef3</citedby><cites>FETCH-LOGICAL-c3703-8b8b21871496e677d7eab073c8a9242aba3c1e8568ed8896718c35234c909ef3</cites><orcidid>0000-0002-9192-9928 ; 0000-0002-9245-440X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fwe.2393$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fwe.2393$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Pliego Marugán, Alberto</creatorcontrib><creatorcontrib>García Márquez, Fausto Pedro</creatorcontrib><title>Advanced analytics for detection and diagnosis of false alarms and faults: A real case study</title><title>Wind energy (Chichester, England)</title><description>Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.</description><subject>Alarms</subject><subject>analytics</subject><subject>Case studies</subject><subject>Condition monitoring</subject><subject>Control systems</subject><subject>Correlation analysis</subject><subject>Curve fitting</subject><subject>Data acquisition</subject><subject>False alarms</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>Maintenance</subject><subject>multivariable analysis</subject><subject>Offshore energy sources</subject><subject>Offshore operations</subject><subject>Pattern recognition</subject><subject>reliability</subject><subject>SCADA</subject><subject>Supervisory control and data acquisition</subject><subject>Turbines</subject><subject>Wind farms</subject><subject>Wind power</subject><subject>wind turbine</subject><subject>Wind turbines</subject><issn>1095-4244</issn><issn>1099-1824</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp10E1Lw0AQBuBFFKxV_AsLHjxI6n4lu-utlPoBBS8FL8Ky2Z1ISprU3cSQf2-aevU0w7wPAzMI3VKyoISwxx4WjGt-hmaUaJ1QxcT51KeJYEJcoqsYd4RQQqmaoc-l_7G1A49tbauhLV3ERROwhxZcWzb1OPfYl_arbmIZcVPgwlYRsK1s2McpLWxXtfEJL3EAW2Fnxzi2nR-u0cWEb_7qHG2f19vVa7J5f3lbLTeJ45LwROUqZ1RJKnQGmZRegs2J5E5ZzQSzueWOgkozBV4pnUmqHE8ZF04TDQWfo7vT2kNovjuIrdk1XRjPiYZxIjOREsFGdX9SLjQxBijMIZR7GwZDiTl-zvRgjp8b5cNJ9mUFw3_MfKwn_QtcGWzp</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Pliego Marugán, Alberto</creator><creator>García Márquez, Fausto Pedro</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-9192-9928</orcidid><orcidid>https://orcid.org/0000-0002-9245-440X</orcidid></search><sort><creationdate>201911</creationdate><title>Advanced analytics for detection and diagnosis of false alarms and faults: A real case study</title><author>Pliego Marugán, Alberto ; García Márquez, Fausto Pedro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3703-8b8b21871496e677d7eab073c8a9242aba3c1e8568ed8896718c35234c909ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Alarms</topic><topic>analytics</topic><topic>Case studies</topic><topic>Condition monitoring</topic><topic>Control systems</topic><topic>Correlation analysis</topic><topic>Curve fitting</topic><topic>Data acquisition</topic><topic>False alarms</topic><topic>Fault detection</topic><topic>Fault diagnosis</topic><topic>Maintenance</topic><topic>multivariable analysis</topic><topic>Offshore energy sources</topic><topic>Offshore operations</topic><topic>Pattern recognition</topic><topic>reliability</topic><topic>SCADA</topic><topic>Supervisory control and data acquisition</topic><topic>Turbines</topic><topic>Wind farms</topic><topic>Wind power</topic><topic>wind turbine</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pliego Marugán, Alberto</creatorcontrib><creatorcontrib>García Márquez, Fausto Pedro</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Wind energy (Chichester, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pliego Marugán, Alberto</au><au>García Márquez, Fausto Pedro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Advanced analytics for detection and diagnosis of false alarms and faults: A real case study</atitle><jtitle>Wind energy (Chichester, England)</jtitle><date>2019-11</date><risdate>2019</risdate><volume>22</volume><issue>11</issue><spage>1622</spage><epage>1635</epage><pages>1622-1635</pages><issn>1095-4244</issn><eissn>1099-1824</eissn><abstract>Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.</abstract><cop>Bognor Regis</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/we.2393</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-9192-9928</orcidid><orcidid>https://orcid.org/0000-0002-9245-440X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1095-4244 |
| ispartof | Wind energy (Chichester, England), 2019-11, Vol.22 (11), p.1622-1635 |
| issn | 1095-4244 1099-1824 |
| language | eng |
| recordid | cdi_proquest_journals_2307645042 |
| source | Access via Wiley Online Library |
| subjects | Alarms analytics Case studies Condition monitoring Control systems Correlation analysis Curve fitting Data acquisition False alarms Fault detection Fault diagnosis Maintenance multivariable analysis Offshore energy sources Offshore operations Pattern recognition reliability SCADA Supervisory control and data acquisition Turbines Wind farms Wind power wind turbine Wind turbines |
| title | Advanced analytics for detection and diagnosis of false alarms and faults: A real case study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T16%3A31%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Advanced%20analytics%20for%20detection%20and%20diagnosis%20of%20false%20alarms%20and%20faults:%20A%20real%20case%20study&rft.jtitle=Wind%20energy%20(Chichester,%20England)&rft.au=Pliego%20Marug%C3%A1n,%20Alberto&rft.date=2019-11&rft.volume=22&rft.issue=11&rft.spage=1622&rft.epage=1635&rft.pages=1622-1635&rft.issn=1095-4244&rft.eissn=1099-1824&rft_id=info:doi/10.1002/we.2393&rft_dat=%3Cproquest_cross%3E2307645042%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2307645042&rft_id=info:pmid/&rfr_iscdi=true |