Review of the application of deep learning for fault detection in wind turbine

Abstract

The stability of the wind turbine is always determined by the nature of the weather and a volatile climate. These natural factors are often affect wind turbine performance. In addition, extreme natural condition can also cause failures in wind turbine components and systems. Therefore, fault detection and condition monitoring are needed as they serve to detect and prevent failures at an early stage. Inspired by the success of deep learning methods, in this paper we present an overview of deep learning applications for detecting and preventing failures in wind turbines. The data preprocessing techniques that help deep learning models perform the tasks also discussed. All major components from reviewed study are summarized in tabular form to facilitate comparison of deep learning methods and data preprocessing techniques for fault detection in wind turbines.