HIGH-VOLTAGE DC (HVDC) FAULT DETECTION USING ADVANCED SENSORS

Abstract

High-voltage direct current (HVDC) transmission systems play a critical role in long-distance power transfer, renewable energy integration, and cross-border grid interconnections. However, their fast-changing fault dynamics and low inherent fault current make timely and accurate fault detection a major challenge. This study proposes an advanced sensor-driven framework for rapid HVDC fault detection using high-resolution optical, magnetic, and intelligent electronic sensors. The approach leverages real-time data acquisition, high-frequency sampling, and feature extraction to identify transient fault signatures with improved precision. Machine-learning-assisted signal analysis is integrated to classify different fault types, such as pole-to-ground, pole-to-pole, and converter station faults, within milliseconds. Experimental validation on a simulated HVDC test environment demonstrates significant improvements in detection speed, sensitivity, and noise immunity compared to conventional threshold-based methods. The results show that advanced sensor fusion effectively enhances system reliability and stability by enabling fast isolation and minimizing the risk of converter damage and power interruption. This research contributes to the development of intelligent protection schemes for next-generation HVDC grids.