Fault classification model of distribution network based on rough neural network and decision tree

被引：0

作者：

Huang, Wensi ^{[1
]}

Lu, Xin ^{[1
]}

Liao, Huadong ^{[1
]}

Hu, Jiandi ^{[1
]}

Chen, Jianjun ^{[1
]}

Yang, Rui ^{[2
]}

Gao, Hongjun ^{[2
]}

机构：

[1] State Grid Xintong Yili Technol, Fuzhou, Fujian, Peoples R China

[2] Sichuan Univ, Chengdu, Peoples R China

来源：

2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020) | 2020年

关键词：

distribution network; classification of defects; feature vector; characteristic parameter;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The proposal of global energy interconnection improves the complexity of power grid operation and increases the possibility of fault occurrence, so the diagnosis of power grid fault has become a more urgent need. In this paper, two kinds of common faults in distribution network are analyzed, and the classification models of these two faults are proposed. One is the classification model of transmission line faults in distribution network. The fault types are determined by inputting the transient signal characteristics of the fault into the rough neural network. The other is to use decision tree to identify the characteristic parameters of transformer after fault, and then to judge the fault type.

引用

页码：219 / 224

页数：6

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