Transferability of Operational Status Classification Models Among Different Wind Turbine Typesq
This work addresses a domain-specific issue for the wind energy industry to improve operations and maintenance, but it is incremental as it builds on existing methods.
The paper tackled the problem of supervised learning models for wind turbine performance status classification not generalizing across different turbine types, and demonstrated successful transferability using power curve alignment and convolutional neural networks with feature-space extension on real-world data.
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.