A New Power System Fault Diagnosis Method Based on Rough Set Theory and Quantum Neural Network

被引：0

作者：

He, Zhengyou ^{[1
]}

Zhao, Jing ^{[1
]}

Yang, Jianwei ^{[1
]}

Gao, Wei ^{[1
]}

机构：

[1] SW Jiaotong Univ, Coll Elect Engn, Chengdu, SC, Peoples R China

来源：

2009 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), VOLS 1-7 | 2009年

关键词：

Rough set theory; Quantum neural network; Fault diagnosis; Fault section estimation;

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：

This paper proposed a novel fault diagnosis scheme for estimating the fault section of power system by using hybrid Rough Set and Quantum Neural Network (RSQNN). The RSQNN approach is developed basing the rough set attributes reduction and quantum neural network recognization. The efficiency and fault tolerance of RSQNN scheme used for fault diagnosis is evaluated in simulation studies, which show promising results that the faults section can be accurately diagnosed in complex power grid and imperfect/uncertain fault information condition.

引用

页码：1327 / 1330

页数：4

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