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

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

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.
Original languageEnglish
Title of host publicationEuropean Symposium on Artificial Neural Networks 2022
PublisherEuropean Symposium on Artificial Neural Networks
Pages357-362
Number of pages5
ISBN (Electronic) 978287587084-1
DOIs
Publication statusPublished - 2022
Event30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022 - Bruges, Belgium
Duration: 5 Oct 20227 Oct 2022

Conference

Conference30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022
CountryBelgium
CityBruges
Period5/10/227/10/22

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