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Energies 2016, 9(4), 280; doi:10.3390/en9040280

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

1
College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
2
Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 1 February 2016 / Revised: 7 March 2016 / Accepted: 7 April 2016 / Published: 12 April 2016
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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. View Full-Text
Keywords: wind turbines; supervisory control and data acquisition (SCADA) data; parallel factor analysis (PARAFAC); K-means clustering; condition monitoring wind turbines; supervisory control and data acquisition (SCADA) data; parallel factor analysis (PARAFAC); K-means clustering; condition monitoring
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, W.; Ma, X. Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines. Energies 2016, 9, 280.

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