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Wind turbine fault detection through principal component analysis and statistical hypothesis testing

Autor
Pozo, F.; Vidal, Y.
Tipus d'activitat
Article en revista
Revista
Advances in science and technology
Data de publicació
2016-10-31
Volum
101
Pàgina inicial
45
Pàgina final
54
DOI
https://doi.org/10.4028/www.scientific.net/AST.101.45 Obrir en finestra nova
Projecte finançador
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
Repositori
http://hdl.handle.net/2117/99593 Obrir en finestra nova
Resum
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. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a ...
Citació
Pozo, F., Vidal, Y. Wind turbine fault detection through principal component analysis and statistical hypothesis testing. "Advances in science and technology", 31 Octubre 2016, vol. 101, p. 45-54.
Paraules clau
Aerodynamics, FAST (Fatigue, Structures and Turbulence), fault detection, principal component analysis, statistical hypothesis testing, wind turbine
Grup de recerca
CoDAlab - Control, Modelització, Identificació i Aplicacions

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