Support Vector Machine Model For Identification And Classification of Various Transmission Line Faults

The article presents a thorough investigation into the realm of fault identification and classification of transmission lines for a reliable and stable power system operation. With the growing demand for efficient electrical energy transmission, the identification and prompt rectification of faults in transmission lines have become imperative to prevent some catastrophic consequences. Therefore, developing a robust and accurate fault detection model is very crucial to maintain the integrity of power grids. In this article various machine learning models have been developed to find most accurate identification of different types of fault and a detail examination with analysis has been introduced. It is seen support vector machine (SVM) model works more superior than other machine learning models and accuracy has been reached more than ninety-nine percent which has been appreciated through corresponding confusion matrix, regression model and statistical analysis.