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.
Highlights
In order to shorten the repair time of faulted electrical transmission lines, efforts have been made to develop reliable and accurate fault location methods [1,2,3]
In order to test the effectiveness of the fault location method in practice, a fault location test was carried out on a medium voltage (MV) cable circuit
This paper proposes a novel traveling-wave-based method for fault location in power cables
Summary
In order to shorten the repair time of faulted electrical transmission lines, efforts have been made to develop reliable and accurate fault location methods [1,2,3]. Most of them are applied for overhead lines: Fault location methods for power cable designs are rarely reported. Actual operating experience indicates that additional effort is still needed to locate short circuit faults in power cable systems more accurately. In Reference [10], a normalized fault location formula, which requires neither an external common time reference nor the traveling wave velocity, was proposed Despite these advantages, the signal time differences used in Reference [10] can be confusing if the fault contains multiple discharges, which is common in power cable faults. This paper introduces an unsupervised learning method for fault location of power cables and to improve on the traditionally applied traveling-wave-based method. The effectiveness of the fault location method has been evaluated by a short-circuit fault test on a cable circuit
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