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Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression

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  • Rogers, T.J.
  • Gardner, P.
  • Dervilis, N.
  • Worden, K.
  • Maguire, A.E.
  • Papatheou, E.
  • Cross, E.J.

Abstract

There exists continued interest in building accurate models of wind turbine power curves for better understanding of performance or assessment of the condition of the turbine or both. Better predictions of the power curve allow increased insight into the operation of the turbine, aid operational decision making, and can be a key feature of online monitoring and fault detection strategies. This work proposes the use of a heteroscedastic Gaussian Process model for this task. The model has a number of attractive properties when modelling power curves. These include, removing the need to specify a parametric functional form for the power curve and automatic quantification of the variance in the prediction. The model exists within a Bayesian framework which exhibits built-in protection against over-fitting and robustness to noisy measurements. The model is shown to be effective on data collected from an operational wind turbine, returning accurate mean predictions (<1% normalised mean-squared error) and higher likelihoods than a corresponding homoscedastic model.

Suggested Citation

  • Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
    • Handle: RePEc:eee:renene:v:148:y:2020:i:c:p:1124-1136
    DOI: 10.1016/j.renene.2019.09.145
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    Cited by:

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    2. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    3. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
    5. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    6. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    7. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    8. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).

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