Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model

J.A. Bilbao Nieva, E. Lourens, Andreas Schulze- Bonhage, Lisa Ziegler

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Wind turbine towers are subjected to highly varying internal loads, characterized by large uncertainty. The uncertainty stems from many factors, including what the actual wind fields experienced over time will be, modeling uncertainties given the various operational states of the turbine with and without controller interaction, the influence of aerodynamic damping, and so forth. To monitor the true experienced loading and assess the fatigue, strain sensors can be installed at fatigue-critical locations on the turbine structure. A more cost-effective and practical solution is to predict the strain response of the structure based only on a number of acceleration measurements. In this contribution, an approach is followed where the dynamic strains in an existing onshore wind turbine tower are predicted using a Gaussian process latent force model. By employing this model, both the applied dynamic loading and strain response are estimated based on the acceleration data. The predicted dynamic strains are validated using strain gauges installed near the bottom of the tower. Fatigue is subsequently assessed by comparing the damage equivalent loads calculated with the predicted as opposed to the measured strains. The results confirm the usefulness of the method for continuous tracking of fatigue life consumption in onshore wind turbine towers.
Original languageEnglish
Article numbere35
Number of pages27
JournalData-Centric Engineering
Issue number3
Publication statusPublished - 2022


  • Fatigue load monitoring
  • Gaussian process
  • input estimation
  • latent force models
  • state estimation


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