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Fault tolerant control of wind turbine via identified fuzzy models prototypes

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main purpose of this study is the comparison of two control strategies of wind turbine 4.8 MW, using fuzzy control and proportional integral control, taking into account eight kinds of faults that can occur in a wind turbine model. A technique based on fault diagnosis has been used to detect and isolate faults actuators and sensors in this system, it's about an observer applied to the benchmark model. The obtained results are presented to validate the effectiveness of this diagnostic method and present the results of the proposed control strategies.
Czasopismo
Rocznik
Strony
3--13
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
  • Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria
  • Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria
  • Faculty of Technology, University of Médéa, 26000 DZ, Algeria
  • Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria
  • Gas Turbine Joint Research Team, University of Djelfa, Djelfa, Algeria
Bibliografia
  • 1. Odgaard PF, Stoustrup J. Fault tolerant wind speed estimator used in wind turbine controllers. The 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, August 29-31. National Autonomous University of Mexico, Mexico City, Mexico., 2012.
  • 2. Ahmet Arda Ozdemir, Peter Seiler, Gary J. Balas. Wind turbine fault detection using counter-based residual thresholding. IFAC Proceedings Volumes 2011;44(1):8289-8294. https://doi.org/10.3182/20110828-6-IT-1002.01758.
  • 3. Ali El Yaakoubi, Kamal Attari, Adel Asselman, Abdelouahed Djebli. Novel power capture optimization based sensor less maximum power point tracking strategy and internal model controller for wind turbines systems driven SCIG. Frontiers in Energy 2019;13:742-756. https://doi.org/10.1007/s11708-017-0462-x.
  • 4. Atif Iqbal, Deng Ying, Adeel Saleem, Muhammad Aftab Hayat, Kashif Mehmood. Efficacious pitch angle control of variable-speed wind turbine using fuzzy based predictive controller. Energy Reports 2020;6:423-427. https://doi.org/10.1016/j.egyr.2019.11.097.
  • 5. Aylin Adem, Ali Çolak, Metin Dağdeviren. An integrated model using SWOT analysis and hesitant fuzzy linguistic term set for evaluation occupational safety risks in life cycle of wind turbine. Safety Science 2018;106:184-190. https://doi.org/10.1016/j.ssci.2018.02.033.
  • 6. Ayoub EL Bakri, Ismail Boumhidi. Fuzzy modelbased faults diagnosis of the wind turbine benchmark. Procedia Computer Science 2018; 127: 464-470. https://doi.org/10.1016/j.procs.2018.01.144.
  • 7. Benchabane F, Titaouine A, Bennis O, Guettaf A, Yahia K, Taibi D. An improved efficiency of fuzzy sliding mode control of permanent magnet synchronous motor for wind turbine generator pumping system. Applied Solar Energy 2012;48:112-117. https://doi.org/10.3103/S0003701X12020089.
  • 8. Bounar N, Labdai S, Boulkroune A, Farza M, M’Saad M. Adaptive fuzzy control scheme for variable speed wind turbines based on a doubly fed induction generator. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 2020; 44: 629-641. https://doi.org/10.1007/s40998- 019-00276-6
  • 9. Carl Svärd, Mattias Nyberg. Automated design of an FDI system for the wind turbine Benchmark. IFAC Proceedings Volumes 2011; 44(1): 8307-8315. https://doi.org/10.3182/20110828-6-IT-1002.00618.
  • 10. Colombo L, Corradini ML, Ippoliti G, Orlando G. Pitch angle control of a wind turbine operating above the rated wind speed: A sliding mode control approach. ISA Transactions 2020; 96: 95-102. https://doi.org/10.1016/j.isatra.2019.07.002.
  • 11. Ester Sales-Setién, Ignacio Peñarrocha-Alós. Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level. Renewable Energy 2020; 146: 1746-1765. https://doi.org/10.1016/j.renene.2019.07.133.
  • 12. Geev Mokryani, Pierluigi Siano, Antonio Piccolo, Fault ride-through enhancement of wind turbines in distribution networks. Journal of Ambient Intelligence and Humanized Computing 2013; 4: 605-611. https://doi.org/10.1007/s12652-012-0162-7.
  • 13. Hamed Badihi, Youmin Zhang, Subhash Rakheja, Pragasen Pillay. Model based fault tolerant pitch control of an offshore wind turbine. IFAC-Papers 2018;51(18):221-226. https://doi.org/10.1016/j.ifacol.2018.09.303.
  • 14. Hamed Habibi, Ian Howard, Silvio Simani. Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review. Renewable Energy 2019;135:877-896. https://doi.org/10.1016/j.renene.2018.12.066.
  • 15. Hossam HH, Mousa, Abdel-Raheem Youssef, Essam EM. Mohamed. Optimal power extraction control schemes for five-phase PMSG based wind generation systems. Engineering Science and Technology, an International Journal 2020; 23(1): 144-155. https://doi.org/10.1016/j.jestch.2019.04.004.
  • 16. Hui Shao, Zhiwei Gao, Xiaoxu Liu, Krishna Busawon. Parameter-varying modelling and fault reconstruction for wind turbine systems. Renewable Energy 2018; 116 (Part B): 145-152. https://doi.org/10.1016/j.renene.2017.08.083.
  • 17. Mansour Sheikhan, Reza Shahnazi, Ali Nooshad Yousefi, An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines. Neural Computing and Applications 2013; 23: 1359-1368. https://doi.org/10.1007/s00521-012-1081-4.
  • 18. Mohamed Abdelkarim Abdelbaky, Xiangjie Liu, Di Jiang. Design and implementation of partial offline fuzzy model-predictive pitch controller for largescale wind-turbines. Renewable Energy 2020; 145: 981-996. https://doi.org/10.1016/j.renene.2019.05.074.
  • 19. Mosayeb Bornapour, Amin Khodabakhshian, Mohammad Reza Esmaili. Optimal Multi-objective Placement of Wind Turbines Considering Voltage Stability, Total Loss and Cost Using Fuzzy Adaptive Modified Particle Swarm Optimization Algorithm. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 2019; 43: 343-359. https://doi.org/10.1007/s40998-018-0105-1.
  • 20. Nassim Laouti, Nida Sheibat-Othman, Sami Othman. Support vector machines for fault detection in wind turbines. IFAC Proceedings, Milan, Italy, 2011; 44(1): 7067-7072. https://doi.org/10.3182/20110828-6-IT-1002.02560.
  • 21. Nassim Laouti, Sami Othman, Mazen Alamir, Nida Sheibat-Othman. Combination of model-based observer and support vector machines for fault detection of wind turbines. International Journal of Automation and Computing 2014; 11: 274-287. https://doi.org/10.1007/s11633-014-0790-9.
  • 22. Lahcène Noureddine, Mustapha Noureddine, Ahmed Hafaifa, Abdellah Kouzou. DWT-PSD extraction feature for defects diagnosis of small wind generator. Diagnostyka;2019;20(3):45-52. https://doi.org/10.29354/diag/110458.
  • 23. Odgaard Peter Fogh, Jakob Stoustrup. Fault tolerant wind speed estimator used in wind turbine controllers. IFAC papers 2012, August 29-31. National Autonomous University of Mexico, Mexico City,https://doi.org/10.3182/20120829-3-MX-2028.00009.
  • 24. Philip Cross, Xiandong Ma. Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines. International Journal of Automation and Computing 2015; 12: 25-34. https://doi.org/10.1007/s11633-014-0863-9.
  • 25. Pongpak Lap-Arparat, Thananchai Leephakpreeda. Real-time maximized power generation of vertical axis wind turbines based on characteristic curves of power coefficients via fuzzy pulse width modulation load regulation. Energy 2019; 182: 975-987. https://doi.org/10.1016/j.energy.2019.06.098.
  • 26. Silvio Simani, Paolo Castaldi. Data-driven and adaptive control applications to a wind turbine benchmark model. Control Engineering Practice 2013;21(12):1678-1693. https://doi.org/10.1016/j.conengprac.2013.08.009.
  • 27. Sy-Jye G, Jung-Hsing C, Chia-Hsin C. Fuzzy duration forecast model for wind turbine construction project subject to the impact of wind uncertainty. Automation in Construction 2017; 81: 401-410. https://doi.org/10.1016/j.autcon.2017.03.009.
  • 28. Viveiros C, Melício R, Igreja JM, Mendes VMF. Performance Assessment of a Wind Turbine Using Benchmark Model: Fuzzy Controllers and Discrete Adaptive LQG. Procedia Technology 2014; 17:487-494. https://doi.org/10.1016/j.protcy.2014.10.257.
  • 29. Xinfang Zhang, Daping Xu, Yibing Liu. Intelligent control for large-scale variable speed variable pitch wind turbines. Journal of Control Theory and Applications 2004;2:305-311. https://doi.org/10.1007/s11768-004-0015-9.
  • 30. Zafer Civelek. Optimization of fuzzy logic (TakagiSugeno) blade pitch angle controller in wind turbines by genetic algorithm. Engineering Science and Technology, an International Journal 2020; 23(1): 1-9. https://doi.org/10.1016/j.jestch.2019.04.010.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7cd8abba-7fa8-4553-8219-c0477e964e18
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