Fault classification in wind turbines using principal component analysis technique

Yichuan Fu, Yuanhong Liu, Zhiwei Gao

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

3 Citations (Scopus)

Abstract

In this study, dimensionality reduction and feature extraction techniques are applied to fault classification for wind turbine systems using principal component analysis (PCA). Experimentations are performed in terms of two topologies of faulty datasets which are respectively actuator effectiveness loss and sinusoidal fault, occurring in a 4.8-MW wind turbine benchmark system. In order to evaluate the PCA technique, additive white Gaussian noise (AWGN) signals are introduced to the aforementioned two faulty conditions respectively. The experimental results demonstrate that PCA not only can extract the significant features but also distinguish different types of fault effectively in this wind turbine benchmark system.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1303-1308
Number of pages6
ISBN (Electronic)9781728129273
DOIs
Publication statusPublished - Jul 2019
Event17th IEEE International Conference on Industrial Informatics, INDIN 2019 - Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2019-July
ISSN (Print)1935-4576

Conference

Conference17th IEEE International Conference on Industrial Informatics, INDIN 2019
CountryFinland
CityHelsinki-Espoo
Period22/07/1925/07/19

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