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Energies 2017, 10(7), 866; https://doi.org/10.3390/en10070866

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

1
Dipartimento di Ingegneria, Università degli Studi di Ferrara, Via Saragat 1E, 44122 Ferrara (FE), Italy
2
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.
Received: 28 April 2017 / Revised: 16 June 2017 / Accepted: 25 June 2017 / Published: 28 June 2017
(This article belongs to the Special Issue Wind Turbine 2017)
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Abstract

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances. View Full-Text
Keywords: fault diagnosis; analytical redundancy; fuzzy logic; neural networks; data-driven approaches; nonlinear geometric approach; wind farm benchmark simulator fault diagnosis; analytical redundancy; fuzzy logic; neural networks; data-driven approaches; nonlinear geometric approach; wind farm benchmark simulator
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Simani, S.; Castaldi, P.; Farsoni, S. Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model. Energies 2017, 10, 866.

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