Open AccessFeature‐clustering‐based single‐line‐to‐ground fault section location using auto‐encoder and fuzzy C‐means clustering in resonant grounding distribution systems
Author(s) -
Gao JianHong,
Guo MouFa,
Shao Xiang,
Chen DuanYu
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12070
Subject(s) - cluster analysis , fault (geology) , computer science , pattern recognition (psychology) , feature extraction , data mining , fuzzy logic , feature (linguistics) , artificial intelligence , node (physics) , real time computing , engineering , linguistics , philosophy , structural engineering , seismology , geology
Many sensors like digital fault indicators (DFIs) have been applied and promoted in distribution systems. The sensors can provide a technical mean for single‐line‐to‐ground (SLG) fault section location, but there are still some feature extraction and fault diagnosis problems. A novel SLG fault section location method utilizing auto‐encoder (AE) and fuzzy C‐means (FCM) clustering is presented in this work. Taking advantage of abundant information provided by DFIs, striking features can be extracted by the AE network, which is different from the artificially designed features that rely on prior knowledge. Compared with the learning‐based methods requiring massive training data, the proposed method only requires the data from one SLG fault. By applying the AE network to the zero‐sequence current measured by DFIs, the SLG fault section location's striking features could be obtained. Through feature classification by FCM clustering without setting threshold, the positional relationship between each detection node and the fault point would be distinguished to locate the fault section. Considering the abnormal communication of DFIs, the experiment proves that the proposed method can work effectively under various fault conditions.