شماره ركورد كنفرانس :
3208
عنوان مقاله :
Intelligent Fault Detection and Diagnosis System Design for Wind Turbine Based on FAST
پديدآورندگان :
Gilani, Z Islamic Azad University South Tehran Branch , Khaloozadeh, H K.N. Toosi University of Technology , Aliyari Shoorehdeli, M K.N. Toosi University of Technology
كليدواژه :
FAST , Multi-Layer Perceptron , Support Vector Machine , Wind turbine , Fault diagnosis
عنوان كنفرانس :
چهارمين كنفرانس بين المللي كنترل، ابزار دقيق و اتوماسيون
چكيده لاتين :
The dynamics of a wind turbine model are
simulated in five different scenarios, i.e., normal operating
conditions, short circuit in generator, wear and Delamination in
gearbox and low-speed-shaft and aerodynamic asymmetry. In this
paper, first of all, Principal Component Analysis is used to extract
features from input data. Secondly, two methods are employed to
isolate faults of different types at different locations: Support
vector machines and Multi-layer perceptron. The proposed
approach are validated on a 1.5-MW threeblade
wind turbine using the National Renewable Energy
Laboratory wind turbine simulator FAST (Fatigue,
Aerodynamics, Structures, and Turbulence) code.
