# Transferability of Operational Status Classification Models Among Different Wind Turbine Typesq

## TL;DR

This paper investigates how to improve the transferability of wind turbine status classification models across different turbine types by proposing normalization and neural network-based methods, demonstrating success on real-world data.

## Contribution

It introduces two novel methods—power curve normalization and a CNN-based approach—for enhancing model transferability across wind turbine types.

## Key findings

- Normalization improves model generalization across turbines.
- CNN-based method achieves robust classification.
- Methods tested successfully on real industrial data.

## Abstract

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.