DefinePK

Abstract

This research investigates the potential of machine learning algorithms to detect and categorize faults in electrical machines and transmission lines. The objective of this project is to utilize a machine learning algorithm that can accurately identify and classify faults in these electrical systems, thereby decreasing downtime and increasing overall system reliability. Further, it involves simulating fault scenarios in a MATLAB Simulink environment to generate datasets, which are then preprocessed and split into training, validation, and testing sets. The Decision trees, XGBoost, k-NN, and random forest Algorithms are employed. Additionally, Simulink is integrated with the best-performing machine-learning algorithm for real-time fault detection. The experimental results suggest that K-Nearest Neighbours (KNN) and Random Forest algorithms outperformed other tested techniques in terms of fault detection and classification in transmission lines and electrical machines.