Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks
Joint Authors
Kavaz, Ayse Gokcen
Barutcu, Burak
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
This paper proposes a method for sensor validation and fault detection in wind turbines.
Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system’s state of health.
Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect.
Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed.
Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim.
Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm.
Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments.
The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.
American Psychological Association (APA)
Kavaz, Ayse Gokcen& Barutcu, Burak. 2018. Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks. Journal of Sensors،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1201579
Modern Language Association (MLA)
Kavaz, Ayse Gokcen& Barutcu, Burak. Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks. Journal of Sensors No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1201579
American Medical Association (AMA)
Kavaz, Ayse Gokcen& Barutcu, Burak. Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1201579
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201579