Wind turbines Fault Detection and identification using Set-Valued Observers

Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection and Isolation (FDI) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The SVO algorithm is built upon these dynamics, taking into account process disturbances, model uncertainty, and measurement noise. The FDI algorithm is assessed within a publicly available benchmark model, using Monte-Carlo simulation runs.

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