Embedded Edge Computing-Based Fault Detection and Isolation in Smart Power Grids

Abstract

ABSTRACT: Fault detection in smart grids has traditionally relied on centralized monitoring, which often suffers from communication delays and bandwidth limitations. This research introduces an embedded edge computing-enabled fault detection and isolation (FDI) system designed to process high-frequency electrical signals at the edge of the network. The proposed method incorporates real-time waveform sampling, multi-resolution signal analysis, and neuralnetwork-based event classification directly within embedded controllers deployed at substations and feeders. By executing analytics at the edge, the system achieves significant improvements in fault localization speed, falsepositive reduction, and situational awareness during cascading outages. Comprehensive hardware-in-loop testing validates the system’s capability to operate under fault noise, harmonics, and rapidly changing grid configurations.

KEYWORDS: Fault detection, Edge computing, Embedded analytics, Smart grid protection, Signal processing