Multiple actuator fault classification in wind turbine systems using multi-linear principal component analysis techniques

Yichuan Fu, Yuanhong Liu, Zhiwei Gao

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

2 Citations (Scopus)

Abstract

Monitoring and fault diagnosis plays a key role in improving the reliability, availability and productiveness of wind turbine systems. When a wind turbine is subjected to multiple faults, it is even more challenging to identify and classify the faults. In this paper, multi-linear principal component analysis (MPCA) is employed to extract the significant features of a wind turbine for the purpose of fault classification of multiple faults. Simulations and validations are performed in terms of faulty data sets generated by a 4.8-MW wind turbine benchmark system subjected to two actuator faults.

Original languageEnglish
Title of host publicationICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
EditorsHui Yu
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781861376664
DOIs
Publication statusPublished - Sep 2019
Event25th IEEE International Conference on Automation and Computing, ICAC 2019 - Lancaster, United Kingdom
Duration: 5 Sep 20197 Sep 2019

Publication series

NameICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing

Conference

Conference25th IEEE International Conference on Automation and Computing, ICAC 2019
CountryUnited Kingdom
CityLancaster
Period5/09/197/09/19

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