Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification

Abstract

This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262 The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model. One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity. Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade: Inflow velocity Angle of attack Lift coefficient Drag coefficient Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.