Title :
Robust observer-based fault detection via evolutionary optimization with applications to wind turbine systems
Author :
Yongjia Zhu ; Zhiwei Gao
Author_Institution :
Fac. of Math. & Phys. Sci., Univ. of Coll. London, London, UK
Abstract :
In this paper, a robust fault detection filter is designed for a 3MW wind turbine system. The parameter eigenvalue assignment approaches and evolutionary optimal algorithms are integrated to seek optimal observer gain such that the residual signal is sensitive to the fault, but robustness against the disturbances. From all the simulations, the performance of fault detections is satisfactory.
Keywords :
eigenvalues and eigenfunctions; evolutionary computation; genetic algorithms; observers; power generation control; robust control; wind turbines; evolutionary optimal algorithms; parameter eigenvalue assignment; power 3 MW; robust fault detection filter; robust observer based fault detection; wind turbine systems; Actuators; Eigenvalues and eigenfunctions; Fault detection; Observers; Optimization; Robustness; Wind turbines; fault diagnosis; genetic algorithm; parameter eigenvalue assignment; robust observer design; wind turbine systems;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
DOI :
10.1109/ICIEA.2014.6931428
