Data-driven techniques for the fault diagnosis of a wind turbine benchmark

• Autorzy: Simani S. , Farsoni S. , Castaldi P.

Identyfikatory

DOI

10.2478/amcs-2018-0018

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

fault diagnosis; analytical redundancy; fuzzy system; neural network; residual generator; fault estimation; wind turbine benchmark

PL

diagnostyka uszkodzeń; redundancja analityczna; system rozmyty; sieć neuronowa; estymacja błędu; turbina wiatrowa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2018
• Tom: Vol. 28, no. 2
• Strony: 247--268
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Simani S.

• Department of Engineering, University of Ferrara, Via Saragat 1/E, 44124 Ferrara, Italy

autor

Farsoni S.

• Department of Engineering, University of Ferrara, Via Saragat 1/E, 44124 Ferrara, Italy

autor

Castaldi P.

• Department of Electronics, Computer Science and Systems, University of Bologna, Via Fontanelle 40, 47100 Forlì, Italy

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).