Efficient Fault Localization in Smart Grid Through Analysis of the Wave Matrix Image Using Convolutional Neural Networks

This article presents a methodology for fault localization in electric power distribution systems through the analysis of wave matrix image using Convolutional Neural Networks (CNN). Ensuring a continuous and high-quality supply of electric power is crucial for the efficient operation of a Power System. However, the extensive coverage of the Electric System makes it susceptible to various disturbances caused by factors such as adverse weather conditions, equipment failures, presence of animals in the networks, and human errors. These disturbances can result in faults, characterized as short-circuits or abnormal currents between conductors or to the ground. With approximately 80% of power supply interruptions attributed to Distribution System faults, the need for effective fault localization methods is evident. By extracting voltage signal characteristics at measurement points during disturbances and transforming them into a panoramic representation of the system in the form of an image, the panoramic image generation, derived from simulated short-circuit values at each system bus, provides a comprehensive visualization of the network, enabling precise fault localization. The results demonstrate the accuracy of the CNN method in fault localization. This approach offers scalability, efficient transformation into images, and precise fault localization in electric power distribution systems.