Loading...
Loading...

Go to the content (press return)

Wind turbine fault detection through principal component analysis and multivariate statistical inference

Author
Pozo, F.; Vidal, Y.; Acho, L.
Type of activity
Presentation of work at congresses
Name of edition
8th European Workshop on Structural Health Monitoring
Date of publication
2016
Presentation's date
2016-07
Book of congress proceedings
8th European Workshop on Structural Health Monitoring (EWSHM 2016): Bilbao, Spain, 5-8 July 2016
First page
1
Last page
10
Project funding
Control, dinàmica i aplicacions
DPI2014-58427-C2-1-R, Desarrollo y validación de sistemas de detección de fallos y diseño de estrategias de control tolerante a fallos con aplicación a plantas de energía eólica offshore
Design of advanced control strategies and fault detection for complex mechatronic systems
Repository
http://hdl.handle.net/2117/91344 Open in new window
http://www.ndt.net/events/EWSHM2016/app/content/Paper/210_Pozo.pdf Open in new window
Abstract
This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the wind turbine is inspected or supervised, new measurements are obtained and project...
Citation
Pozo, F., Vidal, Y., Acho, L. Wind turbine fault detection through principal component analysis and multivariate statistical inference. A: "8th European Workshop on Structural Health Monitoring": Bilbao, Spain, 5-8 July 2016.
Keywords
fault detection, multivariate statistical hypothesis testing, principal component analysis, wind turbine
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

Attachments