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An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes
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 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), IEEE , 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2018.
Emneord [en]
condition monitoring system, neural networks, the wavelet transform, and wind power
HSV kategori
Identifikatorer
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
Konferanse
2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC)
Merknad

QC 20181107

Tilgjengelig fra: 2018-11-07 Laget: 2018-11-07 Sist oppdatert: 2018-11-07bibliografisk kontrollert

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