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Open AccessArticle

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

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Dipartimento di Ingegneria, Università degli Studi di Ferrara, Via Saragat 1E, 44122 Ferrara (FE), Italy
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Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “Guglielmo Marconi”—DEI, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 2, 40136 Bologna (BO), Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(4), 783; https://doi.org/10.3390/app9040783
Received: 21 December 2018 / Revised: 4 February 2019 / Accepted: 15 February 2019 / Published: 22 February 2019
(This article belongs to the Special Issue Offshore Wind Energy)
Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines. View Full-Text
Keywords: fault diagnosis; analytical redundancy; fuzzy prototypes; neural networks; diagnostic residuals; fault reconstruction; offshore wind turbine simulator fault diagnosis; analytical redundancy; fuzzy prototypes; neural networks; diagnostic residuals; fault reconstruction; offshore wind turbine simulator
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MDPI and ACS Style

Simani, S.; Castaldi, P. Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System. Appl. Sci. 2019, 9, 783.

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