Fault Detection in Electrical Grids: Harnessing Machine Learning for Enhanced Reliability

This study focuses on enhancing the reliability and redundancy of electrical systems through machine learning-based fault detection. This project’s objective is to create an inexpensive system for early detection of faults in electrical systems. This study proposed a methodology combining machine learning techniques and feature engineering. A dataset containing 12,001 sensor readings each consisting of six values was examined and analyzed by machine-learning techniques. Results showed the system's success in detecting faults with an accuracy of 99% from only six readings. In economically underserved regions, a common trend is the presence of less sophisticated electrical infrastructures. These systems translate into more frequent and longer power outages, a serious concern when essential institutions like hospitals rely on them to function. This research contributes to the field of fault detection by offering a practical and effective way to improve fault detection in underserved regions.