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A detailed understanding of wind turbine performance status classificationcan improve operations and maintenance in the wind energy industry. Due todifferent engineering properties of wind turbines, the standard supervisedlearning models used for classification do not generalize across data setsobtained from different wind sites. We propose two methods to deal with thetransferability of the trained models: first, data normalization in the form ofpower curve alignment, and second, a robust method based on convolutionalneural networks and feature-space extension. We demonstrate the success of ourmethods on real-world data sets with industrial applications.

Transferability of Operational Status Classification Models Among Different Wind Turbine Typesq