Wind turbine fault detection through principal component analysis and statistical hypothesis testing
Visualitza/Obre
10.4028/www.scientific.net/AST.101.45
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/99593
Tipus de documentArticle
Data publicació2016-10-31
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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. 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 statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.
CitacióPozo, 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.
ISSN1662-0356
Fitxers | Descripció | Mida | Format | Visualitza |
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CIMTEC2016_POZ_VID_FD.pdf | 179,1Kb | Visualitza/Obre |