Designing an IoT device for monitoring power transmission lines

Continuous and reliable monitoring of electrical transmission lines is vital to minimize energy losses, shorten fault detection times, and enhance grid resilience. Current monitoring systems, particularly in remote and hard-to-access areas, lack efficiency due to battery life limitations and dependency on external power sources. This study aims to develop a selfsustaining sensor system capable of real-time data collection and analysis by harvesting magnetic energy from overhead power lines. The core of the system is an energy harvester that converts the magnetic field generated around transmission lines into electrical energy. High-permeability magnetic cores and multilayer coil designs were employed to harvest 3-15 mW of energy from currents ranging between 10-500 A. The harvested energy is optimized to power a low-power microcontroller (e.g., ARM Cortex-M0+) and integrated sensor modules, including current transformers, MEMS-based vibration sensors, digital thermistors, and humidity sensors. A hybrid energy storage unit, combining supercapacitors for sudden load demands and lithium-ion batteries for long-term energy needs, ensures uninterrupted operation. Low-power wide-area network technologies (LoRaWAN and NB-IoT) were selected for wireless communication, enabling sensor data transmission to a cloud-based analytics platform at 5-30-minute intervals. On this platform, machine learning algorithms (e.g., anomaly detection, LSTM) analyze real-time grid conditions (overloads, mechanical deformations, insulation leaks) and generate trend reports. This study presents an innovative framework that eliminates external energy dependency in grid monitoring, reduces maintenance costs, and enhances grid sustainability. The system serves as a reference for future research in autonomous monitoring, fault prediction, and energy efficiency within smart grid applications. Additionally, the hybrid energy storage and management strategy developed here establishes a methodological foundation for self-powered IoT systems in similar applications.