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Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data

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  • Moynihan, Bridget
  • Tronci, Eleonora M.
  • Hughes, Michael C.
  • Moaveni, Babak
  • Hines, Eric

Abstract

Offshore wind turbines (OWTs) can be equipped with two types of monitoring systems: (1) a Supervisory Control and Data Acquisition (SCADA) system that monitors operational data such as wind speed and power generation, and (2) vibration sensors like accelerometers and strain gauges to track structural dynamics. While strain gauges enable fatigue damage calculations, not all OWTs in a wind farm have these sensors installed. This paper proposes a Gaussian process regression (GPR) strategy to predict the bending moment time-histories of an OWT. The model takes into account various SCADA data such as wind speed, power, and nacelle acceleration as input and learns to predict the high and low-frequency dynamic response of the system. The strategy is implemented and tested to predict the bending moment response of a 6 MW offshore wind turbine in the fore-aft and side-side directions of the turbine. The accuracy and reliability of the proposed strategy are evaluated and demonstrated considering different operational conditions and multiple hotspot locations along the height of the turbine. The proposed strategy proves to be an efficient virtual sensing strategy and can be easily transferred to other turbines in the same wind farm without the need for widespread installation of strain gauges.

Suggested Citation

  • Moynihan, Bridget & Tronci, Eleonora M. & Hughes, Michael C. & Moaveni, Babak & Hines, Eric, 2024. "Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005317
    DOI: 10.1016/j.renene.2024.120466
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    References listed on IDEAS

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    1. de N Santos, Francisco & D’Antuono, Pietro & Robbelein, Koen & Noppe, Nymfa & Weijtjens, Wout & Devriendt, Christof, 2023. "Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks," Renewable Energy, Elsevier, vol. 205(C), pages 461-474.
    2. Moynihan, Bridget & Mehrjoo, Azin & Moaveni, Babak & McAdam, Ross & Rüdinger, Finn & Hines, Eric, 2023. "System identification and finite element model updating of a 6 MW offshore wind turbine using vibrational response measurements," Renewable Energy, Elsevier, vol. 219(P1).
    3. Avendaño-Valencia, Luis David & Abdallah, Imad & Chatzi, Eleni, 2021. "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression," Renewable Energy, Elsevier, vol. 170(C), pages 539-561.
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