Abstract

This work introduces a statistical method that identifies wind states present in the wind farm La Rumorosa by analyzing wind speed and nacelle position (wind direction). These states contribute to the generation of wind power in microscale, mesoscale, and macroscale phenomena. The data were obtained from five wind turbines at the onshore and anemometric tower in La Rumorosa located on the border with the state of California, USA. The contribution of wind states and their impact on the annual power production in the wind farm was observed using this method. It is concluded that the method reliably identifies wind patterns with low computational effort.

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