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Location of Single-Line-to-Ground Fault Using 1-D Convolutional Neural Network and Waveform Concatenation in Resonant Grounding Distribution Systems | IEEE Journals & Magazine | IEEE Xplore

Location of Single-Line-to-Ground Fault Using 1-D Convolutional Neural Network and Waveform Concatenation in Resonant Grounding Distribution Systems

Publisher: IEEE

Abstract:

Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location....View more

Abstract:

Nowadays, smart monitoring devices such as digital fault indicator (DFI) have been installed in distribution systems to provide sufficient information for fault location. However, it is still a challenge to extract effective features from massive data for single-line-to-ground (SLG) fault-section location. This work proposes a novel method of fault-section location using a 1-D convolutional neural network (1-D CNN) and waveform concatenation. After SLG fault occurs, DFI measures the transient zero-sequence currents at double-ends of the line section, which could be concatenated to construct characteristic waveform. The features of characteristic waveforms would be extracted adaptively by 1-D CNN to locate the fault section. Furthermore, the problem where the on-site recorded data are hard to collect would be solved because 1-D CNN only needs a small number of samples for training in practical applications. The experimental results verified that the proposed method could work effectively under various fault conditions, even if a few DFIs are out of order.
Article Sequence Number: 3501009
Date of Publication: 03 August 2020

ISSN Information:

Publisher: IEEE

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