Transferability of Operational Status Classification Models Among Different Wind Turbine Typesq

Autor:	Trstanova, Z., Martinsson, A., Matthews, C., Jimenez, S., Leimkuhler, B., Van Delft, T., Wilkinson, M.
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1088/1742-6596/1222/1/012041
Popis:	A detailed understanding of wind turbine performance status classification can improve operations and maintenance in the wind energy industry. Due to different engineering properties of wind turbines, the standard supervised learning models used for classification do not generalize across data sets obtained from different wind sites. We propose two methods to deal with the transferability of the trained models: first, data normalization in the form of power curve alignment, and second, a robust method based on convolutional neural networks and feature-space extension. We demonstrate the success of our methods on real-world data sets with industrial applications. Comment: 9 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.08901 Zobrazit plný text záznamu View this record from Arxiv