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An anomaly detection approach based on machine learning and scada data for condition monitoring of wind turbines
KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknisk teori och konstruktion.
Greenbyte AB, Gothenburg, Sweden.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Elektroteknisk teori och konstruktion.ORCID-id: 0000-0003-4763-9429
2018 (engelsk)Inngår i: 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents an anomaly detection approach using machine learning to achieve condition monitoring for wind turbines. The approach applies the information in supervisory control and data acquisition systems as data input. First, machine learning is used to estimate the temperature signals of the gearbox component. Then the approach analyzes the deviations between the estimated values and the measurements of the signals. Finally, the information of alarm logs is integrated with the previous analysis to determine the operation states of wind turbines. The proposed approach has been tested with the data experience of a 2MW wind turbine in Sweden. The result demonstrates that the approach can detect possible anomalies before the failure occurrence. It also certifies that the approach can remind operators of the possible changes inside wind turbines even when the alarm logs do not report any alarms.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2018.
Emneord [en]
And supervisory control and data acquisition systems, Condition monitoring, Electricity generation, Machine learning, Preventive maintenance, Alarm systems, Artificial intelligence, Learning systems, SCADA systems, Wind turbines, Anomaly detection, Data input, Measurements of, On-machines, Operation state, Scada datum, Temperature signal
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-238018DOI: 10.1109/PMAPS.2018.8440525Scopus ID: 2-s2.0-85053114161ISBN: 9781538635964 OAI: oai:DiVA.org:kth-238018DiVA, id: diva2:1278938
Konferanse
2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018, 24 June 2018 through 28 June 2018
Merknad

Conference code: 138773; Export Date: 30 October 2018; Conference Paper; Funding details: CSC, China Scholarship Council; Funding text: This work is financed by Chinese Scholarship Council.

QC 20190115

Tilgjengelig fra: 2019-01-15 Laget: 2019-01-15 Sist oppdatert: 2019-01-15bibliografisk kontrollert

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Totalt: 109 treff
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