Wind Farm Prediction of Icing Based on SCADA Data

Yujie Zhang, Mario Rotea () and Nasser Kehtarnavaz
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Yujie Zhang: Center for Wind Energy, Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Mario Rotea: Center for Wind Energy, Mechanical Engineering Department, University of Texas at Dallas, Richardson, TX 75080, USA
Nasser Kehtarnavaz: Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA

Energies, 2024, vol. 17, issue 18, 1-16

Abstract: In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine.

Keywords: farm-level icing prediction; decision fusion for wind farm icing prediction; feature fusion for wind farm icing prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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