Smart Distribution Grid Fault Localization Using IoT Sensors and Mathematical Load Modeling

Abstract

This study explores the application of Internet of Things (IoT) sensors and mathematical load modeling for fault localization in modern smart distribution grids. As power systems grow more complex due to aging infrastructure and the integration of distributed energy resources, traditional fault detection methods become increasingly inefficient. The proposed approach leverages real-time data from IoT sensors to enhance grid observability and enable precise identification of faults through analysis of electrical parameters. By integrating these data with mathematical load models, the system can simulate grid behavior, detect anomalies, and estimate fault locations with improved accuracy. Additionally, the incorporation of machine learning techniques supports predictive analysis and adaptive fault management, contributing to the development of self-healing grids. Despite challenges such as cybersecurity risks, interoperability, and implementation costs, the approach offers significant benefits in improving reliability, reducing outage duration, and enhancing operational efficiency. This framework is particularly relevant for developing regions, where resilient and intelligent grid systems are essential to meet growing energy demands.