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An anomaly detection approach based on machine learning and scada data for condition monitoring of wind turbines
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.
Greenbyte AB, Gothenburg, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Electromagnetic Engineering.ORCID iD: 0000-0003-4763-9429
2018 (English)In: 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [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
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238018DOI: 10.1109/PMAPS.2018.8440525Scopus ID: 2-s2.0-85053114161ISBN: 9781538635964 (print)OAI: oai:DiVA.org:kth-238018DiVA, id: diva2:1278938
Conference
2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018, 24 June 2018 through 28 June 2018
Note

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

Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-01-15Bibliographically approved

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Cui, YueBertling Tjernberg, Lina

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
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  • Other locale
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Output format
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