A Signal Segmentation Approach to Identify Incident/Reflected Traveling Waves for Fault Location in Half-Bridge MMC-HVdc Grids

Abstract

This article presents a new systematic technique for identifying voltage traveling-waves (TWs) to determine the location of line faults in half-bridge modular multilevel converterbased high-voltage direct-current (HBMMC-HVDC) grids. In this technique, the buffered voltage signal frame around the faultdetection time is first scaled and then segmented via an optimization process. Finally, the incident/reflected TWs arrival times are obtained by executing a simple search algorithm on the reconstructed signal segments' differences. This article describes how to use this technique in three forms of TW-based fault location schemes, including the single-ended scheme with known TW velocity, the double-ended scheme with known TW velocity, and the double-ended scheme with unknown TW velocity. The application results on a 4-terminal HBMMC-HVDC grid simulated with exact component models show the proposed technique's high capability and accuracy in all the three TWbased fault-location schemes. According to these results, the average fault-location errors are less than 0.5% for all the schemes. The numerical results also confirm that the proposed technique maintains its excellent performance, even in the face of close to terminal faults with distances down to 4 km, faults with high resistances up to 450 , and noisy signals with signal-to-noise ratios down to 55 dB. Moreover, the comparison results confirm that the proposed approach is more tolerant of measurement noise than the wavelet transform.

Implementation of a High-Accuracy Neural Network-Based Pupil Detection System for Real-Time and Real-World Applications

“…Image classification enjoys increased accuracy thanks to AI technologies [ 30 ]. Finally, signal segmentation becomes more efficient through AI algorithms, thus improving data analysis processes [ 31 ]. This work aims to contribute to the development of this innovative technology and explore its potential in the context of pupil detection, as part of a larger effort to bring significant benefits to society in line with current advances in AI.…”