Open AccessSCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements
Author(s) -
Francisco de Nolasco Santos,
Nymfa Noppe,
Wout Weijtjens,
Christof Devriendt
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/2/022020
Subject(s) - thrust , scada , structural engineering , turbine , artificial neural network , strain gauge , marine engineering , environmental science , engineering , computer science , mechanical engineering , electrical engineering , machine learning
In this contribution SCADA data and thrust attained through strain measurements are used to train a neural network model which predicts the thrust load of an offshore wind turbine. The model is subsequently cross-validated for different turbines with SCADA data outside of the training period as input and the thrust load from strain measurements as the expected output, and the impact of wind speed and different operating conditions studied. The results for the model, such as MAE, are kept generally under 2 %. The estimated thrust load signal is then converted into the damage equivalent stress caused by the quasi-static load, allowing to quantify the damage induced by the thrust load. The model performed, in general, well, but some over-/underpredictions are severely amplified when converting the loads into the damage equivalent stress.