Wireless Power Line Monitoring Node With Fault Prediction

Abstract

Reliable and uninterrupted electric power distribution is one of the most important requirements of modern society. Residential homes, commercial establishments, hospitals, educational institutions, and industrial sectors depend on continuous electricity supply for their daily operations. Any interruption in the distribution network may cause financial losses, operational delays, equipment malfunction, and inconvenience to consumers. Power distribution lines are often exposed to several risks such as voltage fluctuations, overload current conditions, short circuits, conductor heating, smoke generation, and accidental fire incidents. If these abnormalities are not detected at an early stage, they may lead to serious equipment damage, transformer failures, line breakdown, and public safety hazards. Therefore, continuous monitoring of distribution lines has become an essential requirement for modern power systems.

Traditional monitoring methods such as manual inspection, wired supervisory systems, and SCADA-based infrastructure are expensive and usually concentrated only at substations or major control points. These methods are not suitable for real-time monitoring at the distribution pole level where many faults begin. To overcome these limitations, this paper presents a Wireless Power Line Monitoring Node with Fault Prediction using ESP32 microcontrollers and ESP-NOW communication technology. The proposed system continuously monitors voltage, current, smoke, and flame conditions through distributed wireless sensor nodes. The collected information is transmitted to a central server node for real-time analysis and alert generation.

Whenever abnormal parameters are detected, the system activates a buzzer, displays warning messages on an LCD screen, and sends Telegram notifications to maintenance personnel. The use of wireless communication eliminates complex cabling, reduces installation cost, and enables deployment of multiple monitoring nodes across wide areas. Experimental observations show that the system offers quick fault indication, reliable communication, and effective preventive maintenance support. The proposed solution is economical, scalable, and highly suitable for smart grid monitoring applications.