Abstract
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 supervisory 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 (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.
Highlights
T HE idea of using supervisory control and data acquisition (SCADA) measurements for structural health monitoring (SHM) and condition monitoring has received attention from both the wind energy and structural engineering communities, for the monitoring of critical infrastructures [1]
Artificial neural networks (ANNs) and Gaussian processes (GPs) were used to build a reference power curve for each of the 48 turbines existing in the farm and as well as extreme value statistics (EVS) via an optimization algorithm in order to define alarm thresholds
The results showed that most models were very robust with the highest mean-square error (MSE) error to be 4.8291, and this was happening when the model trained in turbine 4 was predicting power from turbine 3
Summary
Abstract—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. As human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more near densely populated areas, can be seen as an attractive choice. This paper presents an analysis of supervisory 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 predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm.
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