September 12, 2025Wind energy scienceOpen Access

Wind turbine wake detection and characterisation utilising blade loads and SCADA data: a generalised approach

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The framework achieves 77% accuracy in wake detection, indicating its effectiveness under varied conditions.
A convolutional neural network performs precise pattern recognition, distinguishing between various wake states.
The wake characterisation stage adapts to turbulence changes, allowing dynamic tracking of wake position.
Mean RMSE of wind field estimation is 3.7%, showcasing the model's reliability in performance across multiple trials.

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This framework employs SCADA data and neural networks to enhance wake detection and characterisation, improving operational efficiency.

Fojcik et al. (2025) studied this question.

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