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

Yichuan Fu, Yuanhong Liu, Aihua Zhang, Zhiwei Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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 data-driven 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.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3761-3766
Number of pages6
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - Oct 2019
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 14 Oct 201917 Oct 2019

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
CountryPortugal
CityLisbon
Period14/10/1917/10/19

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