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

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ID: 257972
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.
Reference Key
zhang2016energiessimultaneous Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Wenna Zhang;Xiandong Ma
Journal acs combinatorial science
Year 2016
DOI
10.3390/en9040280
URL
http://www.mdpi.com/1996-1073/9/4/280
Keywords
k-means clustering wind turbines condition monitoringtechnology

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