基于概率神经网络的广域后备保护故障判别研究

广域后备保护采集各节点相关信息，以判别电网某区域的故障元件。利用PNN的良好分类和容错能力，提出了基于PNN的广域后备保护故障判别新方法。以线路故障方向元件、线路距离Ⅱ段测量元件、主保护动作状态为PNN网络输入，利用确定故障下的状态信息矩阵作为训练样本，训练PNN网络；再用随机故障时的元件状态信息向量作为测试样本，通过大量仿真实验，模拟了多种信息不准确情况下的故障判别结果。实验证明基于PNN网络的广域后备保护故障判别，具有很好的容错性和正判能力。...

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Bibliographic Details
Published in电力系统保护与控制 Vol. 40; no. 7; pp. 43 - 49
Main Author 吴浩 李群湛 夏焰坤 刘炜
Format Journal Article
LanguageChinese
Published 西南交通大学电气工程学院,四川成都610031 2012
四川理工学院自动化与电子信息学院,四川自贡643000%西南交通大学电气工程学院,四川成都,610031
Subjects
PNN网络
元件状态信息
容错性
广域后备保护
故障判别
PNN网络
广域后备保护
容错性
元件状态信息
故障判别
Online AccessGet full text
ISSN1674-3415
DOI10.3969/j.issn.1674-3415.2012.07.008

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Summary:广域后备保护采集各节点相关信息，以判别电网某区域的故障元件。利用PNN的良好分类和容错能力，提出了基于PNN的广域后备保护故障判别新方法。以线路故障方向元件、线路距离Ⅱ段测量元件、主保护动作状态为PNN网络输入，利用确定故障下的状态信息矩阵作为训练样本，训练PNN网络；再用随机故障时的元件状态信息向量作为测试样本，通过大量仿真实验，模拟了多种信息不准确情况下的故障判别结果。实验证明基于PNN网络的广域后备保护故障判别，具有很好的容错性和正判能力。
Bibliography:WU Hao1,2, LI Qtm-zhan, XIA Yan-kun1, LIU Wei1 （1. Electrical Engineering Institute, Southwest Jiaotong University, Chengdu 610031, China; 2. School of Automation and Electronic Information Engineering, Sichuan University of Science ＆ Engineering, Zigong 643000, China）
41-1401/TM
wide area backup protection, device status informatiom PNN network; fault identification： fault tolerance performance
Information of each node is collected by wide-area backup protection to determine the fault components of some regional power grid. In this paper, a new method of fault identification for wide area backup protection is proposed by using PNN which has good ability of classification and fault-tolerance. The line fault directional component, the measurement element of distance protection paragraph II and the status of main protection are taken as PNN network＇s input, and the state information matrix for all fault is made as the training sample to train PNN network; then the state information vector of some random failure is
ISSN:1674-3415
DOI:10.3969/j.issn.1674-3415.2012.07.008