AUTHOR=Tang Mingzhu , Zhao Qi , Wu Huawei , Wang Ziming , Meng Caihua , Wang Yifan TITLE=Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis JOURNAL=Frontiers in Energy Research VOLUME=9 YEAR=2021 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.751066 DOI=10.3389/fenrg.2021.751066 ISSN=2296-598X ABSTRACT=

Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.