Publication
Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach
Journal Article (2015)
Journal
Annual Reviews in Control
Pages
59-69
Volume
40
Doc link
http://dx.doi.org/10.1016/j.arcontrol.2015.08.002
File
Authors
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Fernandez-Cantí, Rosa M.
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Blesa Izquierdo, Joaquim
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Tornil Sin, Sebastian
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Puig Cayuela, Vicenç
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 scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.
Categories
control theory.
Author keywords
fault detection and isolation, Bayesian reasoning, set-membership approaches, wind turbine benchmark, uncertainty
Scientific reference
R.M. Fernandez-Cantí, J. Blesa, S. Tornil-Sin and V. Puig. Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach. Annual Reviews in Control, 40: 59-69, 2015.
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