Fusion of Electrical and Optical Methods in the Detection of Partial Discharges in Dielectric Oils Using YOLOv8

Partial discharges (PDs) are critical phenomena in diagnosing the insulation condition of electrical equipment using dielectric oils. Their early detection and characterization are fundamental for the prevention of catastrophic failures. This work presents an innovative bimodal approach for laboratory PD analysis through the training of a convolutional neural network (CNN) based on YOLOv8. Firstly, a conventional DDX-type PD electrical detector is enhanced by endowing it with smart capabilities. Through the CNN training, a system is then developed capable of automatically reading and interpreting data dis-played on an electrical detector screen, such as discharge magnitude, pulse count, and applied voltage. This transforms a conventional instrument into an autonomous source of digitized and structured data. The mean precision in the training was 0.91. Concurrently, an optical visualization system using a high-resolution camera is employed to capture direct images of PDs occurring in the dielectric oil. These images provide complementary qualitative and quantitative information, enabling the classification of discharge types based on their visual characteristics. For electrical voltages of 10, 13 and 16 kV, PDs were detected with confidence scores of up to 0.92. This study therefore combines quantitative information intelligently extracted from an electrical detector, with qualitative and mor-phological characterization obtained through optical analysis.