Fault classification in wind turbines using principal component analysis technique

Yichuan Fu; Yuanhong Liu; Zhiwei Gao

doi:10.1109/INDIN41052.2019.8972303

Fault classification in wind turbines using principal component analysis technique

Yichuan Fu, Yuanhong Liu, Zhiwei Gao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 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 language	English
Title of host publication	Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1303-1308
Number of pages	6
ISBN (Electronic)	9781728129273
DOIs	https://doi.org/10.1109/INDIN41052.2019.8972303
Publication status	Published - Jul 2019
Event	17th IEEE International Conference on Industrial Informatics, INDIN 2019 - Helsinki-Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019

Publication series

Name	IEEE International Conference on Industrial Informatics (INDIN)
Volume	2019-July
ISSN (Print)	1935-4576

Conference

Conference	17th IEEE International Conference on Industrial Informatics, INDIN 2019
Country/Territory	Finland
City	Helsinki-Espoo
Period	22/07/19 → 25/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Additive white Gaussian noise (AWGN)
Dimensionality reduction
Fault classification
Feature extraction
Principal component analysis (PCA)
Wind turbines

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10.1109/INDIN41052.2019.8972303

Cite this

Fu, Y., Liu, Y., & Gao, Z. (2019). Fault classification in wind turbines using principal component analysis technique. In Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019 (pp. 1303-1308). Article 8972303 (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2019-July). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972303

@inproceedings{f5374ea905294ac7b8b9c177d474afe8,

title = "Fault classification in wind turbines using principal component analysis technique",

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.",

keywords = "Additive white Gaussian noise (AWGN), Dimensionality reduction, Fault classification, Feature extraction, Principal component analysis (PCA), Wind turbines",

author = "Yichuan Fu and Yuanhong Liu and Zhiwei Gao",

note = "Funding Information: ACKNOWLEDGEMENTS The authors would like to thank the research support from the E\&E faculty at University of Northumbria (UK), and the National Nature Science Foundation of China (NNSFC) under grant 61673074.; 17th IEEE International Conference on Industrial Informatics, INDIN 2019 ; Conference date: 22-07-2019 Through 25-07-2019",

year = "2019",

month = jul,

doi = "10.1109/INDIN41052.2019.8972303",

language = "English",

series = "IEEE International Conference on Industrial Informatics (INDIN)",

publisher = "IEEE",

pages = "1303--1308",

booktitle = "Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019",

address = "United States",

}

Fu, Y, Liu, Y & Gao, Z 2019, Fault classification in wind turbines using principal component analysis technique. in Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019., 8972303, IEEE International Conference on Industrial Informatics (INDIN), vol. 2019-July, IEEE, Piscataway, NJ, pp. 1303-1308, 17th IEEE International Conference on Industrial Informatics, INDIN 2019, Helsinki-Espoo, Finland, 22/07/19. https://doi.org/10.1109/INDIN41052.2019.8972303

Fault classification in wind turbines using principal component analysis technique. / Fu, Yichuan; Liu, Yuanhong; Gao, Zhiwei.
Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019. Piscataway, NJ: IEEE, 2019. p. 1303-1308 8972303 (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fault classification in wind turbines using principal component analysis technique

AU - Fu, Yichuan

AU - Liu, Yuanhong

AU - Gao, Zhiwei

N1 - Funding Information: ACKNOWLEDGEMENTS The authors would like to thank the research support from the E&E faculty at University of Northumbria (UK), and the National Nature Science Foundation of China (NNSFC) under grant 61673074.

PY - 2019/7

Y1 - 2019/7

N2 - 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.

AB - 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.

KW - Additive white Gaussian noise (AWGN)

KW - Dimensionality reduction

KW - Fault classification

KW - Feature extraction

KW - Principal component analysis (PCA)

KW - Wind turbines

UR - https://www.scopus.com/pages/publications/85075784031

U2 - 10.1109/INDIN41052.2019.8972303

DO - 10.1109/INDIN41052.2019.8972303

M3 - Conference contribution

AN - SCOPUS:85075784031

T3 - IEEE International Conference on Industrial Informatics (INDIN)

SP - 1303

EP - 1308

BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019

PB - IEEE

CY - Piscataway, NJ

T2 - 17th IEEE International Conference on Industrial Informatics, INDIN 2019

Y2 - 22 July 2019 through 25 July 2019

ER -

Fault classification in wind turbines using principal component analysis technique

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UN SDGs

Keywords

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