Robust fault estimation in wind turbine systems using GA optimisation

Odofin, Sarah, Gao, Zhiwei and Sun, Kai (2015) Robust fault estimation in wind turbine systems using GA optimisation. In: 13th IEEE Conference on Industrial Informatics (INDIN), 22 - 24 July 2015, Cambridge.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/INDIN.2015.7281798

Abstract

Wind turbine system is a safety-critical system, which
has the demand to improve the operating reliability and reducing the cost caused by the shut-down time and component repairing. As a result, condition monitoring and fault diagnosis have received much attention for wind turbine energy systems. Noticing that environmental disturbances are unavoidable, therefore how to improve the robustness of a fault diagnosis scheme against disturbances/noises has been a key issue in fault
diagnosis community. In this investigation, a robust fault
estimation approach with the aid of eigenstructure assignment and genetic algorithm (GA) optimization is presented so that the estimation error dynamics has a good robustness against disturbances. A simulation study is carried out for a 5MW wind turbine dynamic model, which has demonstrated the effectiveness of the proposed techniques.

Item Type: Conference or Workshop Item (Lecture)
Subjects: H100 General Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Physics and Electrical Engineering
Depositing User: Zhiwei Gao
Date Deposited: 19 Nov 2015 16:45
Last Modified: 19 Nov 2015 16:45
URI: http://nrl.northumbria.ac.uk/id/eprint/24605

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