Hybrid dynamic data-driven approach for drift-like fault detection in wind turbines

Author:

Toubakh Houari,Sayed-Mouchaweh Moamar

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference23 articles.

1. Blesa J, Puig V, Romera J, Saludes J (2011) Fault diagnosis of wind turbines using a set-membership approach. In: Proceedings of IFAC world congress 18. Milan, Italy, pp 8316–8321

2. Chen W, Ding S, Sari A, Naik A, Khan A, Yin S (2011) Observer-based FDI schemes for wind turbine benchmark. In: Proceedings of IFAC world congress, 18. Milan, Italy, pp 7073–7078

3. Costa BSJ, Angelov PP, Guedes LA (2014) Real-time fault detection using recursive density estimation. In: Proceedings of control, automation and electrical systems 25:428–437

4. Kim K, Parthasarathy G, Uluyol O, Foslien W, Shuangwen S, Fleming P (2011) Use of SCADA Data for failure detection in wind turbines. In: Proceedings of the energy sustainability conference and fuel cell conference, Washington, USA, pp 1–9

5. Kolev D, Angelov P, Markarian G, Suvorov M, Lysanov S (2013) ARFA: automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation. In: Proceedings of evolving and adaptive intelligent systems, Singapore, pp 91–97

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