A Signal Segmentation Approach to Identify Incident/Reflected Traveling-Waves for Fault Location in Half-Bridge MMC-HVDC Grids
Farshad, Mohammad; Karimi, Mazaher (2021-12-31)
Farshad, Mohammad
Karimi, Mazaher
IEEE
31.12.2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202201041196
https://urn.fi/URN:NBN:fi-fe202201041196
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vertaisarvioitu
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©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Tiivistelmä
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 converter-based high-voltage direct-current (HBMMC-HVDC) grids. In this technique, the buffered voltage signal frame around the fault-detection 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 TW-based 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 Ω, 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.
Kokoelmat
- Artikkelit [2604]