Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Conor McKinnon, James Carroll, Alasdair McDonald, Sofia Koukoura and Charlie Plumley
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Conor McKinnon: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK
James Carroll: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK
Alasdair McDonald: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK
Sofia Koukoura: Wind and Marine Energy Systems CDT, University of Strathclyde, Glasgow G1 1RD, UK
Charlie Plumley: Cubico Sustainable Investments, Harman House, 1 George St, Uxbridge UB8 1QQ, UK

Energies, 2021, vol. 14, issue 20, 1-20

Abstract: Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

Keywords: isolation forest; wind turbine; condition monitoring; pitch system; SCADA data; anomaly detection (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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