ADVANCING POWER GRID RELIABILITY: A REVIEW OF IOT-DRIVEN FAULT DETECTION IN TRANSMISSION LINES

Abstract

This paper examines the integration of Internet of Things (IoT) technologies into power transmission systems for enhancing fault detection capabilities. With a focus on improving the reliability and efficiency of power transmission networks, we present a thorough review and analysis of IoT-based fault detection methods specifically tailored for transmission lines. Traditional fault detection methods face challenges such as manual inspections, limited real-time monitoring, and susceptibility to errors. In contrast, IoT offers a solution by enabling real-time monitoring and automated fault detection through sensors, communication infrastructure, and data analytics. Our proposed IoT-based fault detection framework encompasses sensors, communication infrastructure, and a data analytics platform. Various sensors, including temperature, vibration, and current sensors, are strategically deployed along transmission lines to monitor key parameters indicative of fault conditions. These sensors collect realtime data, transmitted through wireless communication protocols to a centralized data analytics platform.Machine learning algorithms analyze this data to identify potential fault conditions accurately. Through case studies and experimental deployments, we demonstrate the effectiveness of IoT-based fault detection systems in reducing outage durations, minimizing maintenance costs, and improving the reliability of power transmission networks. Future research opportunities include integrating advanced sensors, edge computing, and standardized communication protocols to enhance system accuracy and scalability. This paper contributes to advancing the reliability and resilience of power transmission lines through IoTdriven fault detection.