Wind Turbine Damage Detection through Convolutional Neuronal Network

Jose Eduardo Castillo Morales, Perfecto Malaquas Quintero Flores, Jose Crispn Hernandez Hernandez, Alberto Reyes Ballesteros, Edmundo Bonilla Huerta

Abstract: the wind turbines now a days are alternative means to obtain renewable energy, nonetheless, due to the geometry and the environment they are usually affected by different factors such as corrosion caused by the contact with the saltines of the liquids or fractures caused by torsion generated by the wind. Because of this, they require constant monitoring to avoid damaging the integrity of a whole park. This article presents the implementation of a convolutional neuronal network to detect and classify the damages in the external structure of a wind turbine, using different algorithms like image processing filters in-between the layers, MaxPooling, Kernel, SoftMax y Backpropagation.

Keywords: convolutional neuronal network, wind turbines, image processing, damages