Real-Time Fault Detection in Power Distribution Systems Using IoT and Machine Learning for Smart Grid Reliability

Abstract

The modernization of electrical power distribution networks through smart grid initiatives demands robust, real-time monitoring and fault detection mechanisms. Traditional systems based on manual inspections and threshold based SCADA alerts are limited by latency, inefficiency, and a lack of adaptability. The integration of Internet of Things (IoT) devices and machine learning (ML) algorithms offers a transformative approach for enhancing situational awareness, predictive maintenance, and anomaly detection in real time. This paper explores an end-to-end framework that leverages sensor enabled distribution networks, edge computing, and advanced ML models for detecting faults with high precision and low latency. The study evaluates various supervised and unsupervised algorithms, including Random Forests, Support Vector Machines, and Autoencoders, applied to time-series sensor data from smart meters and reclosers. The system’s performance is tested in simulated environments resembling real-world fault scenarios such as short circuits, voltage sags, line-to-ground faults, and open circuits. Results indicate that combining IoT and ML not only improves reliability but also facilitates proactive grid management through scalable, cost-effective solutions. The paper concludes with implementation challenges, security concerns, and future directions for intelligent distribution system monitoring.

Keywords:Smart grid, Anomaly detection, SCADA, Predictive maintenance, IoT, Machine learning