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Title: Fault diagnosis of a wind turbine simulated model via neural networks
Authors: Simani, Silvio
Turhan, Cihan
Keywords: Fault diagnosis
Fault estimation
Wind turbines
Neural networks
Robustness and reliability
Publisher: IFAC Secretariat
Source: Simani, S., and Turhan, C. (2018). Fault diagnosis of a wind turbine simulated model via neural networks. IFAC-PapersOnLine, 51(24), 381-388. doi:10.1016/j.ifacol.2018.09.605
Abstract: The fault diagnosis of wind turbine systems 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 wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances.
ISSN: 2405-8963
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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