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Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures

Author

Listed:
  • Conor McKinnon

    (Centre for Doctoral Training of Wind and Marine Energy Systems, University of Strathclyde, Glasgow G1 1RD, UK
    These authors contributed equally to this work.)

  • Alan Turnbull

    (Centre for Doctoral Training of Wind and Marine Energy Systems, University of Strathclyde, Glasgow G1 1RD, UK
    These authors contributed equally to this work.)

  • Sofia Koukoura

    (Centre for Doctoral Training of Wind and Marine Energy Systems, University of Strathclyde, Glasgow G1 1RD, UK)

  • James Carroll

    (Centre for Doctoral Training of Wind and Marine Energy Systems, University of Strathclyde, Glasgow G1 1RD, UK)

  • Alasdair McDonald

    (Centre for Doctoral Training of Wind and Marine Energy Systems, University of Strathclyde, Glasgow G1 1RD, UK)

Abstract

Operations and Maintenance (O&M) can make up a significant proportion of lifetime costs associated with any wind farm, with up to 30% reported for some offshore developments. It is increasingly important for wind farm owners and operators to optimise their assets in order to reduce the levelised cost of energy (LCoE). Reducing downtime through condition-based maintenance is a promising strategy of realising these goals. This is made possible through increased monitoring and gathering of operational data. SCADA data are useful in terms of wind turbine condition monitoring. This paper aims to perform a comprehensive comparison between two types of normal behaviour modelling: full signal reconstruction (FSRC) and autoregressive models with exogenous inputs (ARX). At the same time, the effects of the training time period on model performance are explored by considering models trained with both 12 and 6 months of data. Finally, the effects of time resolution are analysed for each algorithm by considering models trained and tested with both 10 and 60 min averaged data. Two different cases of wind turbine faults are examined. In both cases, the NARX model trained with 12 months of 10 min average Supervisory Control And Data Acquisition (SCADA) data had the best training performance.

Suggested Citation

  • Conor McKinnon & Alan Turnbull & Sofia Koukoura & James Carroll & Alasdair McDonald, 2020. "Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures," Energies, MDPI, vol. 13(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4745-:d:412271
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    References listed on IDEAS

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    Cited by:

    1. Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    2. Wang, Ziqi & Liu, Changliang & Yan, Feng, 2022. "Condition monitoring of wind turbine based on incremental learning and multivariate state estimation technique," Renewable Energy, Elsevier, vol. 184(C), pages 343-360.
    3. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    4. Bruce Stephen, 2022. "Machine Learning Applications in Power System Condition Monitoring," Energies, MDPI, vol. 15(5), pages 1-2, March.
    5. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
    6. Xiangqing Yin & Yi Liu & Li Yang & Wenchao Gao, 2022. "Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting," Energies, MDPI, vol. 15(17), pages 1-22, August.

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