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Drivetrains play an essential role in guaranteeing the reliability of wind turbines. A challenge in their design is the wide range of loading conditions they are ex-posed to. Several design load cases are required to be simulated in order to ensure that the ultimate loads are not exceeded, and to validate that the fatigue lifetime matches the design requirements. These loading conditions include among other (emergency) stops, start-ups, and normal and parked operation in different ambi-ent conditions (wind speed, wave height, …). The design requirements are vali-dated through a combination of functional, robustness and system tests when the turbine is operated in the design load cases. Within the context of Industry 4.0, turbines are becoming increasingly equipped with sensors. This offers opportuni-ties for the in-depth validation of design hypotheses, as it allows to obtain de-tailed insights in the occurrence of loading events to which turbines are exposed to throughout their lifetime. This can be incorporated in future design iterations to further optimize the design based on more realistic loading conditions. The goal of this paper is to automatically and continuously classify SCADA data of an offshore farm in the aforementioned design load cases on a farm-wide level. Us-ing this framework, the effects of wake on loading conditions will be assessed in a data-driven manner.
|Title of host publication||Conference for wind turbine drivetrains 2021|
|Place of Publication||Forschung im Ingenieurwesen|
|Number of pages||6|
|Publication status||Published - Mar 2021|
|Event||Conference for Wind Power Drives 2021|
Duration: 9 Mar 2021 → 11 Mar 2021
|Name||Forschung im Ingenieurwesen/Engineering Research|
|Conference||Conference for Wind Power Drives 2021|
|Period||9/03/21 → 11/03/21|
- Wind turbine
- Wake effects
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