Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model
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 wi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2022-01-01
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| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673622000387/type/journal_article |
| _version_ | 1811156460547080192 |
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| author | Joaquin Bilbao Eliz-Mari Lourens Andreas Schulze Lisa Ziegler |
| author_facet | Joaquin Bilbao Eliz-Mari Lourens Andreas Schulze Lisa Ziegler |
| author_sort | Joaquin Bilbao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T04:51:58Z |
| format | Article |
| id | doaj.art-d08f935b12954421885c9760e9db7cb5 |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-04-10T04:51:58Z |
| publishDate | 2022-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-d08f935b12954421885c9760e9db7cb52023-03-09T12:31:51ZengCambridge University PressData-Centric Engineering2632-67362022-01-01310.1017/dce.2022.38Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force modelJoaquin Bilbao0https://orcid.org/0000-0002-4195-5271Eliz-Mari Lourens1https://orcid.org/0000-0002-7961-3672Andreas Schulze2Lisa Ziegler3Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands EnBW Energie Baden-Württemberg AG, 20095 Hamburg, GermanyFaculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The NetherlandsEnBW Energie Baden-Württemberg AG, 20095 Hamburg, GermanyEnBW Energie Baden-Württemberg AG, 20095 Hamburg, GermanyWind 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.https://www.cambridge.org/core/product/identifier/S2632673622000387/type/journal_articleFatigue load monitoringGaussian processinput estimationlatent force modelsstate estimation |
| spellingShingle | Joaquin Bilbao Eliz-Mari Lourens Andreas Schulze Lisa Ziegler Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model Data-Centric Engineering Fatigue load monitoring Gaussian process input estimation latent force models state estimation |
| title | Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model |
| title_full | Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model |
| title_fullStr | Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model |
| title_full_unstemmed | Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model |
| title_short | Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model |
| title_sort | virtual sensing in an onshore wind turbine tower using a gaussian process latent force model |
| topic | Fatigue load monitoring Gaussian process input estimation latent force models state estimation |
| url | https://www.cambridge.org/core/product/identifier/S2632673622000387/type/journal_article |
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