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Fault diagnosis of sensors, actuators and wind turbine system

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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.
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Bibliogr. 18 poz., rys., tab.
  • Faculty of Sciences and Technology, University of Djelfa, BP. 3117 Djelfa 17.000, Algeria
  • University of Paris-Saclay, 91190 Saint-Aubin, France
  • Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Romania
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  • 4. Odgaard PF, Stroustrup J. A benchmark evaluation of fault tolerant wind turbine control concepts. IEEE Transactions on Control Systems Technology, 2014: 1063-6536.
  • 5. Joshuva A, Sugumaran V. Fault diagnostic methods for wind turbine: A review. ARPN Journal of Engineering and Applied Sciences. 2016; 11(7):4654-4668.
  • 6. Kusiak A, Li W. The prediction and diagnosis of wind turbine fault. Renewable Energy. 2011; 36(1):16-23.
  • 7. Chakkor S, Baghouri M, Hajraoui A. Real time remote monitoring and fault detection in wind turbine. International Scholarly and Scientific Research & Innovation, 2014; 8(9).
  • 8. Elijorde F, Kim S, Lee J. A wind turbine fault detection approach based on cluster analysis and frequent pattern mining. KSII Transactions on Internet and Information Systems (TIIS). 2014:664-677.
  • 9. Odgaard PF, Stroustrup J, Kinnaert M. Fault tolerant control of wind turbines: a Benchmark model. IEEE Transactions on Control Systems Technology; 2014; 21(4).
  • 10. Kusiak A, Wenyan, L. The prediction and diagnosis of wind turbine faults. Renewable Energy. 2011; 36(1):16-23.
  • 11. Leahy K, Lily Hu R, Konstantakopoulos LV, Spanos CJ, Agogino AM. Diagnosing wind turbine faults using machine learning techniques applied to operational data. IEEE International Conference on Prognostics and Health Management (ICPHM). 2016.
  • 12. Nadhir A, Naba A, Hiyama T. Intelligent gradient detection on MPPT control for variable speed wind energy conversion system. ACEEE Int. J. on Electrical and Power Engineering. 2011; 2(2).
  • 13. Laouti N. Fault diagnosis by vectors machines supports: application to different nonlinear multivariable systems. French, PhD thesis, University of Claude Bernard Lyon. 2012.
  • 14. Blesa J, Rotondo D, Puig V, Nejjari F. FDI and FTC of wind turbines using the Interval Observer approach and Virtual Actuators/Sensor. Control Engineering Practice. 2013; 24:138-155.
  • 15. Tabatabaeipour SM, Odgaard PF, Thomas PFB. Fault detection of a Benchmark wind turbine using interval analysis, The 2012 American Control Conference (ACC. 2012:4387-4392.
  • 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.
  • 17. Denton T. Advanced automotive fault diagnosis. Routledge, 2012.
  • 18. Diarra R. Fault diagnosis of wind turbine conversion system, French, Master project, Industrial IT, University Ziane Achour of Djelfa, 2015.
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
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