Creation of a library of images of cable line insulation breakdown for neural network training based on data from the study of wave processes in the digital twin of the cable line

Abstract

The aim of the work is to create a library of images of cable line insulation breakdown for training a neural network of a functional unit integrated into a high voltage test facility based on data from the study of wave processes in the digital twin of a cable line. The goal is achieved by determining the characteristic properties of the wave process in a cable line when tested with high direct voltage of negative polarity during field experiments and verifying these results on a mathematical model. The calculated data array on the verified model is the main volume of primary information for creating a library of images of insulation breakdown. The most important and significant results are the creation of a library of images of insulation breakdown and the adaptation of the algorithm for approximating input data with images from the neural network library, the integration of artificial intelligence into the process of determining the location of defective insulation at the test stage. The significance of the results is to reduce the methodological and instrumental error in determining the damage zone, since all stages of data collection and analysis are performed by a functional module built into the test unit, in addition, the calculation is performed automatically, which minimizes the role of anthropogenic factors on the result and reduces the requirements for the personnel of the electrical laboratory, the introduction of such equipment will reduce complete the time of elimination of the accident and increase the speed of restoration work on the route of the line.