A performance monitoring approach for the novel Lillgrund offshore wind farm

The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically-growing interest in exploring and investing in such offshore power plants as the continent’s water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore their reliability is crucial if they ever need to be fully integrated into the energy arena.

This paper presents an analysis of supervisor control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine learning approach is applied in order to produce individual and population-based power curves, and then predict measurements of the power produced from each wind turbine from the measurements of the other wind turbines in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.

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