Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles
present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS
image sensor is used. Partial discharge measurements using that image sensor are validated by a
standard electrical detection system that uses a discharge capacitor. In order to accurately identify the
images corresponding to partial discharges, a convolutional neural network is trained using a large set
of images captured by the image sensor. An image classification model is also developed using deep
learning with a convolutional network based on a TensorFlow and Keras model. The classification
results of the experiments show that the accuracy achieved by our model is around 95% on the
validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method
has been developed that is based on the use of an image sensor and the design of a convolutional
neural network. This approach allows us to obtain information about the state of mineral oils
before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these
dielectric oils.