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

Wenna Zhang and Xiandong Ma
Additional contact information
Wenna Zhang: College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
Xiandong Ma: Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK

Energies, 2016, vol. 9, issue 4, 1-15

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 (PARAFAC); K -means clustering; condition monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/4/280/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/4/280/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:9:y:2016:i:4:p:280-:d:68035

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().