摘要
提出了一种电力配电网线损计算的RBFNN(Radial basis function neural networks)方法．主要利用RBFNN较强的拟合特性映射线损与特征参数之间复杂的非线性关系,记忆配电线路在结构参数和运行参数变化时线损的规律．采用LBG聚类方法和一种确定最佳聚类数的标准来优化RBFNN隐层节点,以提高网络的利用效率．实例仿真验证了所提方法的有效性和实用性．
A Radial basis function neural networks（RBFNN） method of calculating energy losses in distribution systems is proposed. RBFNN method, due to its strong regression ability, is able to map complex non-linear relation between energy losses and feature parameter in distribution systems, and memorize the rule of energy losses varying with distribution net structure and operation parameters. LBG clustering algorithm and a clustering criterion are used to determine optimal number of hidden nodes of RBFNN, and therefore the use efficiency of the RBFNN is improved. Simulation has verified the validity and practicability of the proposed method.
出处
《自动化学报》
EI
CSCD
北大核心
2007年第3期334-336,共3页
Acta Automatica Sinica
基金
天津大学留学回国人员基金项目(200447)资助~~
关键词
RBF神经网络
聚类算法
配电网
线损
RBFNN, clustering algorithm, distribution net, energy losses
