Smart Detection And Classification of Electrical Faults Using Machine Learning

Abstract

The application of machine learning methods and techniques for the identification of power transmission line defects is the main topic of this research. In contrast to the fast growth in power consumption in recent decades, transmission capacity has not kept pace with this expansion. The most frequent transmission line issues and how to classify them using machine learning are covered in this study. An accurate result is produced by analyzing the flaws with various combinations of inputs using the given methodologies. Spyder IDE, which stands for Scientific Python Development Environment, is where the machine learning techniques are carried out. This strategy is designed to tackle the target. Machine learning, decision tree models, LSTM (long short-term memory), KNN (k-nearest neighbor), and SVM (support vector machine) are some of the terms used in this context.