Modern industrial and urban infrastructures are increasingly reliant on underground electrical grids to ensure public safety and minimize power losses associated with traditional overhead lines. However, the transition to underground cable (UGC) systems introduces a significant operational bottleneck: identifying and repairing faults is substantially more difficult and time-consuming than in visible overhead systems. Current maintenance methodologies remain predominantly reactive or manual, often resulting in prolonged factory downtimes, high repair costs, and increased safety risks for workers. This paper describes the development of an innovative, cost-effective Internet of Things (IoT) architecture designed to automate fault monitoring and provide a predictive pathway for grid maintenance. The proposed system integrates Potential Transformers for continuous real-time voltage monitoring, an Arduino-based ATmega328P microcontroller for signal processing, and a GSM SIM900A module for rapid remote alerting. A specialized Potential Divider (Ohm's Law) method is employed for fault localization, enabling the system to pinpoint anomalies at simulated 1 KM and 2 KM intervals. Experimental evaluation of the hardware prototype demonstrates high reliability, achieving a local response time of 0.8 seconds and remote SMS notification delivery in approximately 4.5 seconds. Beyond reactive detection, this research outlines a strategic roadmap for
Shelar et al. (Wed,) studied this question.
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