Support Vector Machine Parameters Optimization for 500 kV Long OHTL Fault Diagnosis

Abstract

Faults can seriously damage high-voltage (HV) power systems, particularly if they occur on the long overhead transmission line (OHTL) that connects the nuclear power plant (NPP) to the electrical grid. Finding OHTL problems quickly and accurately is essential for the economy, safety, and dependability of the HV power systems. It is essential to pinpoint the problematic phase to avoid unneeded power outages. Thus, one of the most crucial research challenges is now how to identify, classify, and locate OHTL faults. In this study, transient current with high frequency oscillations that arise immediately after a defect at the sending end is investigated in a single-circuit, single-side fed Egyptian 500-kV HV long OHTL. Asymmetric and symmetric faults and locations are also represented in the Alternative Transients Program-Electro Magnetic Transients Program (ATP/EMTP) simulation model under varying fault resistance and inception angles. The proposed solution in this paper is an Optimized Support Vector Machine (OSVM), whose characteristics are optimized via a mutant particle swarm optimization (MPSO) method to detect 500 kV long OHTL faults. The localizer model is also built for practical applications, including power system noise contaminating fault signals. The findings prove that the suggested approach locates the fault in 0.012 seconds from the start of the event, with a 0.0098 percent average percentage error, and without being impacted by differences in fault distance, fault resistance, noise, or fault inception angle. Additionally, the optimised classifier reaches a 99.85% accuracy rate, enhancing line system dependability and advancing nuclear system development.