Application of Artificial Vision Based on Convolutional Neural Networks for Predictive Detection of Faults in Electrical Distribution Line Insulators

Abstract

Insulators play a crucial role in transporting and distributing electrical energy. They separate the energized conductor from the metal structure and support the conductors against adverse weather conditions such as winds and rains. However, these devices lose their insulating and mechanical properties when exposed to climatic factors such as sun exposure, rain, dust, and environmental pollution. This is due to the forming of a cover of organic matter and breaks and fissures, which can trigger adverse effects such as generating electric arcs. For this reason, it is essential to identify these failures effectively. In this research, an innovative solution is proposed that involves the use of artificial vision integrated into uncrewed vehicles, using the YOLOv5 object detection technology based on convolutional neural networks, to analyze 3000 images of the insulators in search of signs of deterioration, such as the presence of organic matter, breaks or cracks. The results showed an accuracy of over 90% in detecting failures. Deploying YOLOv5 alongside an uncrewed vehicle allows for faster and more accurate inspection of insulators along power distribution lines in real-time. Furthermore, by using this artificial vision technology, detailed data on the condition of the insulators can be collected in an automated manner, which facilitates the planning of preventive and corrective maintenance actions. This not only reduces the costs associated with the maintenance of distribution lines but also contributes to improving the reliability and efficiency of the electrical system.