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.
an unmodified (perfect involute) gear pair was compared with that of gears with various profile modifications. relations were found between various profile rnodificatlons and the resulting dynamic loads. the gear's static transmission error, gave a very good estimation of gear dynamic loading. Cor- An effective error, obtained from frequency domain analysis of Design curves generated by dynamic simulation at several profile modlfi- catlons are given for gear systems operated at various applied loads. Optimum profile modifications can then be determined from the design curves to yield a minimum dynamic effect for a gear system and to provide up-to-date knowledge for better gear deslgn. NOMENCLATURE amplitude of ith frequency component