摘要
发输电组合系统可靠性评估中元件状态组合数和潮流计算是引起可靠性评估计算中“计算灾”的主要原因.利用粗糙集的数据约简和特征提取功能,提炼人工神经网络的输入变量、训练样本以及事件类与系统状态之间的概略化关系,建立偶发事件模式识别的粗神经网络(RNN)模型,提出基于RNN的发输电组合系统可靠性评估算法,以提高电力系统可靠性评估的计算效率.可靠性测试系统的计算结果表明,所提算法是正确、可行和有效的.
The number of components states combination and the power flow calculation are main causes producing “computation catastrophe” of reliability assessment calculation about composite generation and transmission systems. The input variables of artificial neural network are reduced, learning samples are extracted, stochastic events are roughly classified, a probable rule set about the relation between stochastic event classes and system states are draw out by means of rough set methods. A contingency pattern identification model-Rough Neural Network (RNN), is presented. Furthermore, a power system reliability evaluation algorithm based on RNN is put forward for increasing the calculation speed of reliability assessment. The numerical experiments for reliability testing systems show the correctness, feasibility and usefulness of the presented method.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第7期38-42,共5页
Journal of Chongqing University
基金
重庆市科学技术计划项目资助(CSTC.2004B2183)
关键词
可靠性
粗糙集
人工神经网
算法
reliability
rough set
artificial neural network
algorithm
