The Autoregresive Analysis for Wind Turbine Signal Postprocessing
Today modern simulations solutions in the wind turbine industry have achieved a high degree of complexity and detail in result. Limitations exist when it is time to validate model results against measurements. Regarding Model validation it is of special interest to identify mode frequencies and to differentiate them from the different excitations. A wind turbine is a complex device and measurements regarding any part of the assembly show a lot of noise. Input excitations are difficult or even impossible to measure due to the stochastic nature of the environment. Traditional techniques for frequency analysis or features extraction are widely used to analyze wind turbine sensor signals, but have several limitations specially attending to non stationary signals (Events). A new technique based on autoregresive analysis techniques is introduced here for a specific application, a comparison and examples related to different events in the wind turbine operations are presented.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055595Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1249
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