NDLI: Diagnosis of the single phase-to-ground fault in distribution network based on feature extraction and transformation from the waveforms

Content Provider	IET Digital Library
Author	Shi, Fang Zhang, Linlin Zhang, Hengxu Xu, Kai Vladimir, Terzija
Abstract	The increasing permeation of the distributed generators in the power system brings great challenges for fault diagnosis, especially for the distribution networks with ungrounded neutral or grounded by Peterson coil as the fault current is limited and easily affected by the noises and interferences. A single phase-to-ground fault section identification method is proposed based on feature extraction of the synchronous waveforms and the calculation of the eigenvalues for the time-sequenced features. First, several fault features are defined and extracted from the synchronous current waveforms obtained by the fault recorders. Then, the topology related fault feature matrix is constructed using the time-series features obtained from different measurement sites, and the eigenvalues of the matrix are calculated based on the random matrix theory. Lastly, using the distribution characteristics of the eigenvalues, improved K-means clustering algorithm is utilised in classifying the fault cases and identifying the faulty sections. The effectiveness of the proposed scheme is verified by IEEE 34 nodes test system and a multi-feeder distribution network.
Starting Page	6079
Ending Page	6086
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 25, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0877
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/25
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-11-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Distributed Generator Distributed Power Generation Distribution Characteristic Distribution Network Earthing Eigen Values Eigen Values And Eigen Function Fault Cases Fault Current Fault Diagnosis Fault Feature Fault Location Fault Recorders Feature Extraction Improved K-means Clustering Algorithm Linear Algebra Multifeeder Distribution Network Numerical Analysis Pattern Clustering Peterson Coil Power Distribution Fault Power System Power System Protection Random Matrix Theory Single Phase-to-ground Fault Section Identification Method Statistics Synchronous Current Waveform Time Series Time-sequenced Feature Time-series Feature Topology Related Fault Feature Matrix
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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