Optimization Analysis of IoT-Based Load Monitoring for Early Fault Detection in PLN ULP Gebang’s Distribution Network - Roy Marcelius Ober Gultom; Solly Aryza; Muhammad Erpandi Dalimunthe (INFOKUM, 2025)
This study aims to analyze and optimize an Internet of Things (IoT)-based load monitoring system for early fault detection in the electrical distribution network at PLN ULP Gebang. The system is developed to monitor electrical parameters—including current, voltage, power, and frequency—in real time using load sensors connected to a microcontroller, with data transmitted to a cloud platform via wireless communication. The collected data is then analyzed to identify anomalies that may indicate early signs of faults, such as phase imbalance, overcurrent, or voltage fluctuations. Experimental results demonstrate that the system can deliver early fault notifications with an accuracy of 92% and significantly reduce response time to field incidents. The implementation of this system has proven effective in improving power supply reliability and enhancing the operational efficiency of PLN ULP Gebang’s distribution personnel.