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Machines 2014, 2(4), 275-298; doi:10.3390/machines2040275

Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

1
Department of Engineering, University of Ferrara, Via Saragat 1E, Ferrara (FE) 44122, Italy
2
Department of Electrical, Electronic and Information Engineering, University of Bologna, Via Fontanelle, 40, Forlì (FC) 47100, Italy
*
Author to whom correspondence should be addressed.
Received: 27 May 2014 / Revised: 10 July 2014 / Accepted: 13 October 2014 / Published: 27 November 2014
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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Abstract

In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution. View Full-Text
Keywords: data-driven approach; fuzzy modeling and identification; fault detection andisolation; reliability and safety; wind turbine benchmark data-driven approach; fuzzy modeling and identification; fault detection andisolation; reliability and safety; wind turbine benchmark
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Simani, S.; Farsoni, S.; Castaldi, P. Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis. Machines 2014, 2, 275-298.

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