Real-time fault detection in power systems using IoT and edge AI

Abstract

This thesis begins with a comprehensive review of the current landscape of smart grid monitoring, identifying the critical limitations of traditional cloud-based architectures, such as high latency, bandwidth congestion, and single points of failure. By synthesizing existing research on fault detection and localization, the study highlights the growing necessity for decentralized processing to handle the high-frequency data generated by modern power systems.

Following this extensive literature evaluation, the research proposes a real-time fault detection framework that integrates the Internet of Things (IoT) and Edge AI. This multi-layered architecture decentralizes computation to an edge layer, enabling autonomous grid monitoring and rapid data handling closer to the source.