Fault Detection and Diagnosis in Power Systems Using Machine Learning

Effective fault identification and diagnosis are critical in modern power systems to ensure operational reliability and reduce economic losses. This research describes a novel approach that uses machine learning techniques, notably Ensemble models, to improve fault detection and classification in electrical networks. Several fault situations were generated using a complete MATLAB/Simulink simulation, which captured important voltage and current data. The study compares the performance of several machine learning techniques, including Support Vector Machines (SVMs) and Ensemble approaches, to various input configurations. The results show that Ensemble models greatly enhance fault detection accuracy and robustness when compared to standard methods, indicating its promise for real world applications in smart grid technology. This study not only tackles the issues of fault management in increasingly complex power systems, but it also establishes a framework for future advances in machine learning applications for electrical infrastructure.