A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wind turbine fault detection through principal component analysis and statistical hypothesis testing
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. ; Peer Reviewed ; Postprint (published version)
Wind turbine fault detection through principal component analysis and statistical hypothesis testing
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. ; Peer Reviewed ; Postprint (published version)
Wind turbine fault detection through principal component analysis and statistical hypothesis testing
2016-01-01
Article (Journal)
Electronic Resource
English
Statistics , Àrees temàtiques de la UPC::Energies::Energia eòlica , statistical hypothesis testing , Estadística aplicada , System failures (Engineering) , FAST (Fatigue , Structures and Turbulence) , Aerodynamics , wind turbine , Classificació AMS::60 Probability theory and stochastic processes , Errors de sistemes (Enginyeria) , fault detection , principal component analysis , Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada
DDC:
690
| British Library Online Contents | 2014
Fault detection based on Kernel Principal Component Analysis
| Online Contents | 2010
Fault detection based on Kernel Principal Component Analysis
| Elsevier | 2010
Texture Analysis for Wind Turbine Fault Detection
| British Library Conference Proceedings | 2016