A New Arc Suppression Method for Single-phase Ground Fault of Distribution Network

Author(s):  
Pei Luo ◽  
Yan Wen ◽  
Yawen Xie
2014 ◽  
Vol 530-531 ◽  
pp. 353-356
Author(s):  
Run Sheng Li

Due to the high ground fault resistance and the complexity of power distribution network structure (such as too many nodes, branches and too long lines), adopting common traveling wave method and ac injection method can not effectively locate the single-phase grounding fault in the distribution network system.To solve above problems and determine the position of the point of failure prisely, this paper adopted the dc location method of injecting the dc signal from the point of failure under the power outage offline. This paper introduces the single phase dc method and the method of three phase dc, and the simulation shows that the dc location method is effective and feasible.


2021 ◽  
Vol 2095 (1) ◽  
pp. 012029
Author(s):  
Yicen Liu ◽  
Xiaojiang Liu ◽  
Songhai Fan ◽  
Xiaomin Ma ◽  
Sijing Deng

Abstract In view of the complex characteristics of nonlinearity and non-stableness of the zero-order current of each line after the single-phase ground fault of the distribution network, a distribution network fault selection method based on Sooty Tern Optimization Algorithm(STOA) and the combination of support vector machine is proposed. At first, the zero-sequence current before and after fault is obtained, then five kinds of IMFs including different components are obtained by ensemble empirical mode decomposition, and the energy entropy of the fault transient zero-sequence current is obtained by Hilbert transform, the results of training and testing are obtained by inputting the feature vector. The simulation results show that the accuracy of the proposed line selection model is 97.5%.


1970 ◽  
Vol 1 (1) ◽  
Author(s):  
Yang Fan

The current distribution network single-phase ground fault detection model knowledge expression is poor, its production process only based on the normal distribution network sample data, no single-phase ground fault data, did not make full use of a prior knowledge, resulting in low detection accuracy. The automatic detection model of single-phase earth fault of new distribution network is proposed. The fault characteristic vector is taken as the input vector, and the degree of matching between the input vector and the weight vector element is introduced as the second layer. The fault vector is used as the input vector, and the fault vector is used as the input vector. Node input, the second layer of the output as the third layer of the input, the model training, the output of the results of the distribution network is a single-phase ground fault detection results. The experimental results show that the proposed model has high detection accuracy. 


Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3917 ◽  
Author(s):  
Yangang Shi ◽  
Tao Zheng ◽  
Chang Yang

Traveling wave (TW)-based fault-location methods have been used to determine single-phase-to-ground fault distance in power-distribution networks. The previous approaches detected the arrival time of the initial traveling wave via single ended or multi-terminal measurements. Regarding the multi-branch effect, this paper utilized the reflected waves to obtain multiple arriving times through single ended measurement. Potential fault sections were estimated by searching for the possible traveling wave propagation paths in accordance with the structure of the distribution network. This approach used the entire propagation of a traveling wave measured at a single end without any prerequisite of synchronization, which is a must in multi-terminal measurements. The uniqueness of the fault section was guaranteed by several independent single-ended measurements. Traveling waves obtained in a real 10 kV distribution network were used to determine the fault section, and the results demonstrate the significant effectiveness of the proposed method.


2020 ◽  
Vol 1656 ◽  
pp. 012008
Author(s):  
Jiran Zhu ◽  
Kai Chen ◽  
Zhonghan Peng ◽  
Hailong Zhang ◽  
Xingchen Wan ◽  
...  

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