摘要
为了实现快速而准确的电网故障诊断,利用广义回归神经网络(GRNN)在逼近能力、分类能力和学习速度方面的优势,建立了基于GRNN的电网故障诊断模型.仿真分析表明:在输入信息因干扰而畸变的情况下,文中所构造的模型能够快速、正确地实现电网的故障诊断;在电网拓扑结构改变的情况下,该模型也具有良好的自适应能力.
In order to realize fast and correct fault diagnosis in electric network, this paper presents a novel fault diagnosis model employing the abilities of GRNN （ General Regression Neural Network） in approximation, classification and learning. Simulated results demonstrate that the proposed model not only can make fast and accurate diagnoses with good fault-tolerance performance, but also possesses excellent self-adaptive ability for various topological structures of electric network.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第9期6-9,共4页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(50477029)
关键词
电力系统
故障诊断
广义回归神经网络
自适应能力
power system
fault diagnosis
general regression neural network
self-adaptive ability
