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

Conor McKinnon, James Carroll, Alasdair McDonald, Sofia Koukoura, David Infield and Conaill Soraghan
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Conor McKinnon: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1XQ, UK
James Carroll: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1XQ, UK
Alasdair McDonald: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1XQ, UK
Sofia Koukoura: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1XQ, UK
David Infield: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1XQ, UK
Conaill Soraghan: Offshore Renewable Energy Catapult, Glasgow G1 1XQ, UK

Energies, 2020, vol. 13, issue 19, 1-19

Abstract: 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.

Keywords: anomaly detection; gearbox; SCADA; condition monitoring; Isolation Forest; One Class Support Vector Machine; Elliptical Envelope (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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