Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Frontiers in Energy Research

Caihua Meng¹  Yifan Wang¹  Mingzhu Tang¹  Qi Zhao¹  Ziming Wang²  Huawei Wu³ 

[1] Changsha, China;Guilin, China;Xiangyang, China;

关键词: wind turbines;    fault diagnosis;    supervised learning;    unsupervised learning;    semi-supervised learning;   

DOI  :  10.3389/fenrg.2021.751066

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY