Abstract
In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location.
Original language | English |
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Article number | 2083 |
Pages (from-to) | 2083-2107 |
Number of pages | 24 |
Journal | Sensors |
Volume | 19 |
Issue number | 9 |
Early online date | 5 May 2019 |
DOIs | |
Publication status | Published - May 2019 |
Keywords
- circuit faults
- fault currents
- fault location
- power cables
- sheath currents
- Power cables
- Fault location
- Sheath currents
- Circuit faults
- Fault currents
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Instrumentation
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
- Biochemistry