SVM–KNN Hybrid Learning Framework for Transmission Line Fault Identification and Localization

Abstract

A paradigm shift in energy grid operations has arisen from swift improvements in measurement and computer technology, allowing contemporary systems to integrate self-healing functionalities. An essential prerequisite for these systems is the fast identification and localisation of transmission line faults to guarantee grid reliability and expedite service restoration. This research introduces an innovative SVKN method for precise detection and localisation of transmission line problems. Voltage and current signals undergo initial preprocessing before being decomposed with the Wild Horse Optimisation (WHO) method to isolate high-frequency features and low-frequency approximations. The amalgamation of Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) facilitates efficient classification and localisation. The findings indicate that the suggested SVKN method surpasses current models such as SVKN, SVM, KNN, Two Stage KNN and Two Stage SVM, in both fault type classification and the identification of the impacted transmission line. The SVKN model attained an accuracy of 94.13% in predicting fault positions, hence validating its precision. The suggested SVKN framework markedly enhances Transmission Line Fault Identification and Localisation, providing a dependable and effective solution for real-time fault management in advanced smart grids.