期刊
ENERGIES
卷 9, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/en9010003
关键词
wind turbine; fault detection; principal component analysis; statistical hypothesis testing; FAST (Fatigue; Aerodynamics; Structures and Turbulence)
资金
- Spanish Ministry of Economy and Competitiveness [DPI2011-28033-C03-01, DPI2014-58427-C2-1-R]
- Generalitat de Catalunya [2014 SGR 859]
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 datathe baseline and the data from the current wind turbineare 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.
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