Investigation of isolation forest for wind turbine pitch system condition monitoring using SCADA data
McKinnon, Conor and Carroll, James and McDonald, Alasdair and Koukoura, Sofia and Plumley, Charlie (2021) Investigation of isolation forest for wind turbine pitch system condition monitoring using SCADA data. Energies, 14 (20). 6601. ISSN 1996-1073 (https://doi.org/10.3390/en14206601)
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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.
ORCID iDs
McKinnon, Conor, Carroll, James ORCID: https://orcid.org/0000-0002-1510-1416, McDonald, Alasdair, Koukoura, Sofia and Plumley, Charlie;-
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Item type: Article ID code: 78144 Dates: DateEvent13 October 2021Published7 October 2021Accepted17 September 2021SubmittedSubjects: Technology > Mechanical engineering and machinery Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 13 Oct 2021 12:58 Last modified: 12 Dec 2024 11:59 URI: https://strathprints.strath.ac.uk/id/eprint/78144