Robust fault estimation in wind turbine systems using GA optimisation

Sarah Odofin, Zhiwei Gao, Kai Sun

Research output: Contribution to conferenceOtherpeer-review

8 Citations (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - Jul 2015
Event13th IEEE Conference on Industrial Informatics (INDIN) - Cambridge
Duration: 1 Jul 2015 → …

Conference

Conference13th IEEE Conference on Industrial Informatics (INDIN)
Period1/07/15 → …

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