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Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data

Author

Listed:
  • Rad Haghi

    (Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada)

  • Cassidy Stagg

    (Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada)

  • Curran Crawford

    (Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8P 5C2, Canada)

Abstract

Assessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.

Suggested Citation

  • Rad Haghi & Cassidy Stagg & Curran Crawford, 2024. "Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data," Energies, MDPI, vol. 17(2), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:346-:d:1316277
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    References listed on IDEAS

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    1. Vera-Tudela, Luis & Kühn, Martin, 2017. "Analysing wind turbine fatigue load prediction: The impact of wind farm flow conditions," Renewable Energy, Elsevier, vol. 107(C), pages 352-360.
    2. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
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    5. Alvarez, Eduardo J. & Ribaric, Adrijan P., 2018. "An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA," Renewable Energy, Elsevier, vol. 115(C), pages 391-399.
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    Cited by:

    1. Zivanovic, Miroslav & Runacres, Mark C., 2024. "Seasonality in synthetic average wind speed," Renewable Energy, Elsevier, vol. 233(C).
    2. García-Vaca, M.A. & Sierra-García, J.E. & Santos, Matilde, 2026. "Probabilistic evaluation for early wind turbine yaw misalignment detection," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).

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