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Simultaneous fault detection and sensor selection for condition monitoring of wind turbines

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

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Simultaneous fault detection and sensor selection for condition monitoring of wind turbines. / Zhang, Wenna; Ma, Xiandong.
In: Energies, Vol. 9, No. 4, 280, 12.04.2016.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Zhang, W & Ma, X 2016, 'Simultaneous fault detection and sensor selection for condition monitoring of wind turbines', Energies, vol. 9, no. 4, 280. https://doi.org/10.3390/en9040280

APA

Zhang, W., & Ma, X. (2016). Simultaneous fault detection and sensor selection for condition monitoring of wind turbines. Energies, 9(4), Article 280. https://doi.org/10.3390/en9040280

Vancouver

Zhang W, Ma X. Simultaneous fault detection and sensor selection for condition monitoring of wind turbines. Energies. 2016 Apr 12;9(4):280. doi: 10.3390/en9040280

Author

Zhang, Wenna ; Ma, Xiandong. / Simultaneous fault detection and sensor selection for condition monitoring of wind turbines. In: Energies. 2016 ; Vol. 9, No. 4.

Bibtex

@article{dde7a983675144e3bbbb427d41964888,
title = "Simultaneous fault detection and sensor selection for condition monitoring of wind turbines",
abstract = "Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring. ",
keywords = "Wind turbines , Supervisory control and data acquisition (SCADA) data, Parallel factor analysis , K-means clustering , Condition monitoring",
author = "Wenna Zhang and Xiandong Ma",
year = "2016",
month = apr,
day = "12",
doi = "10.3390/en9040280",
language = "English",
volume = "9",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "4",

}

RIS

TY - JOUR

T1 - Simultaneous fault detection and sensor selection for condition monitoring of wind turbines

AU - Zhang, Wenna

AU - Ma, Xiandong

PY - 2016/4/12

Y1 - 2016/4/12

N2 - Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

AB - Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

KW - Wind turbines

KW - Supervisory control and data acquisition (SCADA) data

KW - Parallel factor analysis

KW - K-means clustering

KW - Condition monitoring

U2 - 10.3390/en9040280

DO - 10.3390/en9040280

M3 - Journal article

VL - 9

JO - Energies

JF - Energies

SN - 1996-1073

IS - 4

M1 - 280

ER -