Fault Detection and Diagnosis in a 3-Phase Transmission Line Using IoT Technology

This study is inspired by the fact that innovative technology for fault diagnosis is a fundamental and essential part of fault isolation and analysis of faults for the study of power systems using Internet of Things-based technology. This study examined the issue of automatic fault diagnosis. The designed system provides a defect identifier that helps identify and categorize system issues. The primary goal of this study was to locate transmission line faults and report this information to the control room. Transmission lines are employed in this with the assistance of wires connected to a circuit so that the circuit may detect faults and send signals to the microcontroller whenever they occur.The Arduino platform was used for data processing and Internet of Things (IoT) integration. The fault type, line, and range are then displayed on the LCD. The precise position of the issue will then be provided by the GPS module, together with the coordinates, and Node MCU will be used to interact with the control room and transmit the message, which will include information about the approximate location of the overhead transmission line fault (for hardware model) phase short circuits, two-line issue, then two link to milled fault identification will be likely with the last framework plan. A transmission line utilizing MATLAB and Simulink libraries was used to design the model. The novelty of the present work is that a practical fault detection system is designed by integrating multiple sensors and recording IoT data sheets for future use in machine learning models. In the future, it will be strategic to utilize machine learning techniques. Decision trees and random forests were trained using IoT datasheets for power framework checking and transmission line fault identification for line-to-ground, line-to-line, and three-phase faults. Further integration will improve the current system by enabling predictive fault diagnosis.