FAST Wind Turbine simulations for mass and aerodynamic imbalance detection

Abstract

The dataset was created in order to be able to train machine learning models in the domain of wind energy, especially for the use cases of predictive maintenance / condition monitoring and anomaly detection. In the wind energy domain, one usually suffers from two main problems: Firstly, the lack of sufficiently many labeled data instances, especially for rarely occurring anomalous states. Secondly, the stochastic nature of the resulting measurements, as changing and turbulent environmental conditions lead to a lot of noise in the recorded sensor signals, which in turn makes the analysis of the data very hard. To this end, a synthetic wind turbine dataset has been created, which addresses these issues. The dataset allows to obtain a more controlled view than measurements in the field would yield, since for different fixed conditions of the wind turbine, simulations have been carried out for a various number of environmental conditions. The goal is to be able to learn data representations for different states of the wind turbine from the simulated data, which would not be possible on real measurements. Moreover, as the simulations can be carried out with a very high number of different sensor outputs, the dataset offers a possibility to uncover relations between different measurements / components of the wind turbine, which are not known yet. The dataset consists strictly of highdimensional time series as output of certain setups of a 5MW wind turbine. They represent various measurements of important characteristics, such as acceleration or bending moments, on the blades, the nacelle, or different parts of the wind turbine. The dataset is created to resemble different locations and degrees of mass imbalance on one of the three wind turbine blades.