Using forecast information for storm ride-through control

Braulio Barahona Garzón, Pierre-Julien Trombe, Claire Louise Vincent, Pierre Pinson, Gregor Giebel, Nicolaos Antonio Cutululis

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Using probabilistic forecast information in control algorithms can improve the performance of wind farms during periods of extreme winds. This work presents a wind farm supervisor control concept that uses probabilistic forecast information to ride-through a storm with softer ramps of power. Wind speed forecasts are generated with a statistical approach (i.e. time series models). The supervisor control is based on a set of logical rules that consider point forecasts and predictive densities to ramp-down the power of the wind farm before the storm hits. The potential of this supervisor control is illustrated with data from the Horns Rev 1 wind farm, located in the North Sea. To conclude, an overview of ongoing and future research in the Radar@Sea experiment is given. This experiment aims at improving offshore wind power predictability and controllability through the increased use of meteorological information, and particularly weather radar images.
Original languageEnglish
Title of host publicationProceedings of EWEA 2013
Number of pages9
PublisherEuropean Wind Energy Association (EWEA)
Publication date2013
ISBN (Print)978-163266314-6
Publication statusPublished - 2013
EventEuropean Wind Energy Conference & Exhibition 2013 - Vienna, Austria
Duration: 4 Feb 20137 Feb 2013


ConferenceEuropean Wind Energy Conference & Exhibition 2013
Internet address


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