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An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes
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 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an anomaly detection approach using artificial neural networks and the wavelet transform for the condition monitoring of wind turbines. The method aims to attain early anomaly detection and to prevent possible false alarms under healthy operations. In the approach, nonlinear autoregressive neural networks are used to estimate the temperature signals of the gearbox. The Mahalanobis distances are then calculated to measure the deviations between the current states and healthy operations. Next, the wavelet transform is applied to remove noisy signals in the distance values. Finally, the operation information is considered together with the refined distance values to detect potential anomalies. The proposed approach has been tested with the real data of three 2 MW wind turbines in Sweden. The results show that the approach can detect possible anomalies before failure events occur and avoid reporting alarms under healthy operations.

Place, publisher, year, edition, pages
IEEE , 2018.
Keywords [en]
condition monitoring system, neural networks, the wavelet transform, and wind power
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238158DOI: 10.23919/PSCC.2018.8442916ISI: 000447282400160Scopus ID: 2-s2.0-85053108763OAI: oai:DiVA.org:kth-238158DiVA, id: diva2:1261484
Conference
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)
Note

QC 20181107

Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2018-11-07Bibliographically 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
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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