Modeling of quasi-static thrust load of wind turbines based on 1 s SCADA data
A reliable load history is crucial for a fatigue assessment of wind turbines. However, installing strain sensors on every wind turbine is not economically feasible. In this paper, a technique is proposed to reconstruct the thrust load history of a wind turbine based on high-frequency Supervisory...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-03-01
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| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/3/139/2018/wes-3-139-2018.pdf |
| Summary: | A reliable load history is crucial for a fatigue assessment of wind turbines.
However, installing strain sensors on every wind turbine is not economically
feasible. In this paper, a technique is proposed to reconstruct the thrust
load history of a wind turbine based on high-frequency Supervisory Control
and Data Acquisition (SCADA) data. Strain measurements
recorded during a short period of time are used to train a neural network.
The selection of appropriate input parameters is performed based on Pearson
correlation and mutual information. Once the training is done, the model can
be used to predict the thrust load based on SCADA data only. The technique is
validated on two different datasets, one consisting of simulation data (using
the software FAST v8, created by Jonkman and Jonkman, 2016) obtained in a
controllable environment and one consisting of measurements taken at an
offshore wind turbine. In general, the relative error between simulated or
measured and predicted thrust load barely exceeds 15 % during normal
operation. |
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| ISSN: | 2366-7443 2366-7451 |