<p>Rain erosion of wind turbine blades represents an interesting topic of study due to its not negligible impact on annual energy production of the wind farms installed in rainy sites. A considerable amount of recent research works has been oriented to this subject, proposing rain erosion modelling, performance losses prediction, structural issues studies, etc. This work aims to present a new method to predict the damage on a wind turbine blade. The method is based on the coupling of site weather characteristics, computational fluid-particle dynamics simulations, semi-empirical rain erosion modelling, machine learning, and wind turbine dynamics simulations. The coupling of these different techniques results in a fast prediction of the damage, due to the adoption of a machine learning regression method, which keeps a high level of prediction accuracy learned from the fluid-particle simulations. Moreover, it represents a paradigm shift: here numerical simulations are not directly linked to a specific operative condition, they are only used to build a database; then, it will be used to train the machine learnt tool that will be able to predict erosion damage in any possible operative condition.</p><p>The tool is then integrated with the site data (wind and rain), the specific operating conditions of the turbine, and the dynamic simulation of the entire machine (based on the blade element approach). The method is applied here to study the effect of different rain conditions and blade coating materials, on the damage produced by the rain over two representative sections of a reference 5MW turbine blade operating for 1 year in normal turbulence wind conditions.</p>

Machine Learnt Prediction of Rain Erosion Damage on Wind Turbine Blade Sections

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

Turbo Expo 2021

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