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 considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e. the fault estimate, involves data–driven approaches, as they can represent effective tools for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data–driven proposed solutions relies on fuzzy systems 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 along 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 turbine. The achieved performances are also compared with those of other model–based strategies from the related literature. Finally, a Monte–Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.
|Titolo:||Data-Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark|
SIMANI, Silvio (Primo) [Writing – Review & Editing] (Corresponding)
FARSONI, Saverio (Secondo) [Validation]
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||03.1 Articolo su rivista|
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