Transferability of Operational Status Classification Models among Different Wind Turbine Types

Z. Trstanova, A. Martinsson, C. Matthews, S. Jimenez, B. Leimkuhler, T. Van Delft, M. Wilkinson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.

Original languageEnglish
Title of host publicationJournal of Physics: Conference Series, Volume 1222, WindEurope Conference and Exhibition 2019 2–4 April 2019, Bilbao,Spain
Publication statusPublished - 21 May 2019
EventWindEurope Conference and Exhibition 2019 - Bilbao, Spain
Duration: 2 Apr 20194 Apr 2019

Publication series

NameJournal of Physics: Conference Series
PublisherIOP Publishing
ISSN (Print)1742-6588


ConferenceWindEurope Conference and Exhibition 2019


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