- Tytuł:
- Data-driven techniques for the fault diagnosis of a wind turbine benchmark
- Autorzy:
-
Simani, S.
Farsoni, S.
Castaldi, P. - Powiązania:
- https://bibliotekanauki.pl/articles/330715.pdf
- Data publikacji:
- 2018
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
fault diagnosis
analytical redundancy
fuzzy system
neural network
residual generator
fault estimation
wind turbine benchmark
diagnostyka uszkodzeń
redundancja analityczna
system rozmyty
sieć neuronowa
estymacja błędu
turbina wiatrowa - Opis:
- This paper deals with the fault diagnosis of wind turbines and investigates 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 poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely 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 models with exogenous input, 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 247-268
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł