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.
Highlights
Wind power has gained remarkable attention in the past decade because wind energy is one of the rapidly clean energy sources and has received worldwide support for renewable energy development (MUA, 2017)
To further solve the problem of overfitting or underfitting caused by the improper selection of nuclear parameters, Tang et al (2014) proposed a method of Wind turbines (WTs) fault diagnosis based on the Shannon wavelet Support vector machine (SVM) (SWSVM) and manifold learning
In this method, mixed-domain features are extracted to construct a highdimensional feature set, manifold learning is used to compress the high-dimensional feature set into low-dimensional eigenvectors, and low-dimensional eigenvectors are inputted into an SWSVM to recognize WT gearbox faults
Summary
Wind power has gained remarkable attention in the past decade because wind energy is one of the rapidly clean energy sources and has received worldwide support for renewable energy development (MUA, 2017). As the main force of global renewable energy development, China attaches great importance to new energy, especially wind power generation. The large-scale development and utilization of wind energy have brought huge opportunities for the development of the market economy, and raised important crucial challenges related to reliability, cost-effectiveness, and energy blade images of the security. Wind turbines (WTs) are often located in remote areas, operated in harsh working environments for a long time, and have withstood randomly varying weather conditions, wind shear, temperature, wind speed, and load, thereby frequent WT failures. The high cost of operation and maintenance (OM) of WTs underscores the urgency of fault diagnosis. Fault diagnosis and the timely maintenance of WTs can reduce huge financial losses. The fault diagnosis method based on machine learning (ML) is suggested to detect the operating
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