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Energies 2016, 9(1), 3; doi:10.3390/en9010003

Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

Control, Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola Universitària d’Enginyeria Tècnica Industrial de Barcelona (EUETIB), Universitat Politècnica de Catalunya (UPC), Comte d’Urgell, 187, Barcelona 08036, Spain
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Academic Editor: Frede Blaabjerg
Received: 17 November 2015 / Revised: 11 December 2015 / Accepted: 14 December 2015 / Published: 23 December 2015
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Abstract

This paper 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 structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data—the baseline and the data from the current wind turbine—are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines. View Full-Text
Keywords: wind turbine; fault detection; principal component analysis; statistical hypothesis testing; FAST (Fatigue, Aerodynamics, Structures and Turbulence) wind turbine; fault detection; principal component analysis; statistical hypothesis testing; FAST (Fatigue, Aerodynamics, Structures and Turbulence)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pozo, F.; Vidal, Y. Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing. Energies 2016, 9, 3.

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