Minkowski logarithmic error: A physics-informed neural network approach for wind turbine lifetime assessment

Onderzoeksoutput: Conference paper

Samenvatting

In this contribution we present a physics-informed neural network (PINN) approach for wind turbine fatigue estimation. This PINN incorporates physical information of the structure’s fatigue profile in its loss function, referred to as Minkowski logarithmic error (MLE) - an extension of the log loss for any given L p space. The function is mathematically analysed and differentiated in order to better understand its behaviour. The results obtained using the MLE are favourably compared with previous efforts using the mean squared logarithmic error. Finally, the long-term error is evaluated based on the effect of p.
Originele taal-2English
TitelEuropean Symposium on Artificial Neural Networks 2022
UitgeverijEuropean Symposium on Artificial Neural Networks
Pagina's357-362
Aantal pagina's5
ISBN van elektronische versie 978287587084-1
DOI's
StatusPublished - 2022
Evenement30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022 - Bruges, Belgium
Duur: 5 okt 20227 okt 2022

Conference

Conference30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022
Land/RegioBelgium
StadBruges
Periode5/10/227/10/22

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