Fault Detection in Electrical Grids: Harnessing Machine Learning for Enhanced Reliability
DOI:
https://doi.org/10.47611/jsrhs.v13i1.6159Keywords:
Fault Detection, Machine Learning, Neural Network, Electrical SystemAbstract
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.
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Copyright (c) 2024 William Jiang; Alfred Renaud, Guillermo Goldzstein
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