Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity for the planet. It has been shown that by sampling actual blade conditions with drone photography, the rest of the wind turbine blades at the site can be predicted with 86% R-squared error. This result not only saves money on drone inspections but also allows for a more precise estimation of lost revenue and a clearer understanding of repair investments.
Martinez, Casey; Asare Yeboah, Festus; Herford, Scott; Brzezinski, Matt; and Puttagunta, Viswanath
"Predicting Wind Turbine Blade Erosion using Machine Learning,"
SMU Data Science Review: Vol. 2
, Article 17.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss2/17
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License