A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling

Alan Turnbull, James Carroll and Alasdair McDonald
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Alan Turnbull: Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
James Carroll: Institute of Energy and Environment, Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Alasdair McDonald: Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH9 3DW, UK

Energies, 2022, vol. 15, issue 14, 1-13

Abstract: Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria.

Keywords: wind turbine; SCADA; machine learning; temperature; modelling; threshold; alarm (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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