Servicios Personalizados
Revista
Articulo
Indicadores
- Citado por SciELO
- Accesos
Links relacionados
- Citado por Google
- Similares en SciELO
- Similares en Google
Compartir
Tecnura
versión impresa ISSN 0123-921X
Resumen
MANRIQUE PIRAMANRIQUE, Rubén Francisco y SOFRONY ESMERAL, Jorge. Data driven fault detection and isolation: a wind turbine scenario. Tecnura [online]. 2015, vol.19, n.44, pp.71-82. ISSN 0123-921X. https://doi.org/10.14483/udistrital.jour.tecnura.2015.2.a05.
One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI) filters based on "Data Driven" (DD) techniques. In order to enhance FDI performance, a multi-level strategy is used where: (i) the first level detects the occurrence of any given fault (detection), while (ii) the second identifies the source of the fault (isolation). Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models) were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine.
Palabras clave : data mining; fault detection; wind energy.