Usage of a Support Vector Machine Learning Classifier to Identify and Sort Electrical Faults

This research examines the use of a Support Vector Machine (SVM) classifier in the detection and categorization of electrical issues. Electrical systems play a crucial role in several sectors, and the quick identification and categorization of defects are vital for maintaining system dependability. The Support Vector Machine (SVM) classifier is used for the purpose of classifying various fault kinds. This classification is dependent on input characteristics that are collected from sensor data and system parameters. The SVM model is trained using real-time data obtained from electrical systems, which includes measurements of voltage, current, and temperature. This research assesses the efficacy of the Support Vector Machine (SVM) classifier by using several performance measures, including accuracy, precision, recall, and F1 score. The findings illustrate the effectiveness of the Support Vector Machine (SVM) method in effectively detecting and categorising electrical malfunctions. The findings of this study have significant significance for enhancing the effectiveness of defect detection systems in industrial environments, hence helping to the development of more efficient maintenance methods and minimising operational downtime. This work provides a fundamental basis for further investigations into the use of Support Vector Machines (SVM) and other machine learning methodologies in the realm of advanced electrical fault diagnosis and prediction.