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Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Energies (Basel), 2020-10, Vol.13 (19), p.5152 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13195152

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  • Title:
    Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data
  • Author: McKinnon, Conor ; Carroll, James ; McDonald, Alasdair ; Koukoura, Sofia ; Infield, David ; Soraghan, Conaill
  • Subjects: Anomalies ; anomaly detection ; Condition monitoring ; Configurations ; Failure ; Fault detection ; gearbox ; Gearboxes ; Isolation Forest ; Literature reviews ; Neural networks ; One Class Support Vector Machine ; Principal components analysis ; SCADA ; Support vector machines ; Training ; Turbines ; Wind power ; Wind turbines
  • Is Part Of: Energies (Basel), 2020-10, Vol.13 (19), p.5152
  • Description: Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en13195152
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD
    ProQuest Central
    DOAJ Directory of Open Access Journals
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