Name
Abstract | ||
|---|---|---|
This paper presents an end-to-end approach for locating faults on high-voltage dc (HVDC) transmission lines. Different from traditional methods which rely on communications between different measuring units or feature extraction of post fault transients, the proposed algorithm takes the raw data of locally detected traveling current surges as the only-input and outputs the fault locations directly. Especially, the stacked auto-encoder (SAE) is utilized to model the relationship between fault currents and fault locations. The SAE-based method is performed in time domain and tested with a simulated HVDC transmission line modeled in PSCAD/EMTDC. The simulation results show that this method is effective in locating faulted points and robust against attenuation, overlapping of traveling surges, and various ground resistances. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2848841 | IEEE ACCESS |
Keywords | Field | DocType |
VSC-HVDC,fault location,stacked auto-encoder,deep learning | Time domain,Autoencoder,Transmission line,Computer science,Raw data,Electronic engineering,Electric power transmission,Feature extraction,Artificial intelligence,Deep learning,Attenuation,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guomin Luo | 1 | 1 | 2.06 |
| Changyuan Yao | 2 | 0 | 0.68 |
| Yinglin Liu | 3 | 0 | 0.68 |
| Yingjie Tan | 4 | 0 | 1.35 |
| Jinghan He | 5 | 14 | 7.47 |
| Kai Wang | 6 | 1734 | 195.03 |