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Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach

Author
Rosa M. Fernandez-Canti; Blesa, J.; Tornil-Sin, S.; Puig, V.
Type of activity
Journal article
Journal
Annual reviews in control
Date of publication
2015-10-30
Volume
40
First page
56
Last page
59
DOI
https://doi.org/10.1016/j.arcontrol.2015.08.002 Open in new window
Repository
http://hdl.handle.net/2117/83898 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S1367578815000395 Open in new window
Abstract
This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation sche...
Citation
Fernández-Cantí, R. M., Blesa, J., Tornil-Sin, S., Puig, V. Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach. "Annual reviews in control", 30 Octubre 2015, vol. 40, p. 56-59.
Keywords
Bayesian reasoning, Control theory, Fault detection and isolation, Set-membership approaches, Uncertainty, Wind turbine benchmark
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Advanced Control Systems
SIC - Smart Control Systems

Participants