Fault diagnosis of sensors, actuators and wind turbine system

• Autorzy: Azzouzi M. , Diarra R. , Popescu D.

Identyfikatory

DOI

10.29354/diag/93846

• Języki publikacji: EN

Abstrakty

EN

The production capacity of installed wind power greatly increases in worldwide. Hence the interest is focused on the reliability and efficiency of wind turbines; then to reduce the production cost and increase the yield. The main objective of our research in this work is to diagnose wind system. We presented a state of the art of diagnosis approach applied on wind turbines and various occurred faults which should be detected and isolated in the wind turbine parts. After that, an overview on this proposed solution for wind turbines, which opted for a diagnostic strategy based on support vector machines (SVM). A Benchmark of a wind power of 4.5 MW with faults on sensors, actuators and the systems was presented. Defects of the Benchmark are in the pitch system, the drive system, the generator and the converter. We tested then the effectiveness of the used method by visualizing simulation results of diagnosis in two different scenarios.

Słowa kluczowe

EN

wind turbine; benchmark; faults; modeling; diagnosis; SVM; FDI

PL

energia wiatrowa; turbina wiatrowa; niezawodność; wydajność; diagnostyka; czujnik; usterka

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2018
• Tom: Vol. 19, No. 4
• Strony: 3--10
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Azzouzi M.

• Faculty of Sciences and Technology, University of Djelfa, BP. 3117 Djelfa 17.000, Algeria

autor

Diarra R.

• University of Paris-Saclay, 91190 Saint-Aubin, France

autor

Popescu D.

• Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Romania

Bibliografia

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• 16. Tang L, Decastro JA, Zhang X. Diagnosis of engine sensor, actuator and component faults using a bank of adaptive nonlinear estimators, IEEE Aerospace Conference Proceedings. 2011. http://dx.doi.org/10.1109/AERO.2011.5747565
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• 18. Diarra R. Fault diagnosis of wind turbine conversion system, French, Master project, Industrial IT, University Ziane Achour of Djelfa, 2015.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).