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

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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)

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.

Suggested Citation

  • Alan Turnbull & James Carroll & Alasdair McDonald, 2022. "A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling," Energies, MDPI, vol. 15(14), pages 1-13, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5298-:d:868252
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    References listed on IDEAS

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    1. Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    2. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    3. Yonglong Yan & Jian Li & David Wenzhong Gao, 2014. "Condition Parameter Modeling for Anomaly Detection in Wind Turbines," Energies, MDPI, vol. 7(5), pages 1-17, May.
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    1. Alessandro Murgia & Robbert Verbeke & Elena Tsiporkova & Ludovico Terzi & Davide Astolfi, 2023. "Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. Davide Astolfi & Ravi Pandit & Andrea Lombardi & Ludovico Terzi, 2022. "Multivariate Data-Driven Models for Wind Turbine Power Curves including Sub-Component Temperatures," Energies, MDPI, vol. 16(1), pages 1-18, December.

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