Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA)

被引：0

作者：

Fu, Yichuan ^{[1
]}

Liu, Yuanhong ^{[2
]}

Zhang, Aihua ^{[3
]}

Gao, Zhiwei ^{[1
]}

机构：

[1] Univ Northumbria Newcastle, Fac Engn & Environm, Dept Math Phys & Elect Engn, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England

[2] Northeast Petr Univ, Sch Elect Engn & Informat, Daqing 163318, Heilongjiang, Peoples R China

[3] Bohai Univ, Coll Engn, Jinzhou 121000, Liaoning, Peoples R China

来源：

45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019) | 2019年

关键词：

Actuator faults; fault classification; fast Fourier transform (FFT); multi-linear principal component analysis (M-PCA); wind turbines; PCA;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) system and smart meters. It is challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a datadriven fault diagnosis and classification algorithm is addressed by integrating fast Fourier transform (FFT) and multi-linear principal component analysis (MPCA) in order to enhance the capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to a 4.8-MW wind turbine benchmark system, where multiple actuator faults are taken into accounts. The effectiveness of the algorithm is demonstrated by intensive simulations and comparison studies.

引用

页码：3761 / 3766

页数：6

共 17 条

[1] PCA-based multivariate statistical network monitoring for anomaly detection [J].