电测与仪表2018，Vol.55Issue(11)：7-11,18,6.
基于RBF神经网络和无迹变换法的三相概率潮流计算
Three-phase probabilistic power flow calculation based on RBF neural network and unscented transformation algorithm
摘要
Abstract
Unbalanced load,asymmetric network parameters and access to renewable energy are being connected to distribution network,and the three-phase probability load flow is introduced.In this paper,a new three-phase probabilistic power flow calculation method is proposed,and the RBF neural network and the unscented transform method are used to solve the three-phase probabilistic load flow.Firstly,the Sigma matrix and the corresponding weights of the input variables are obtained by using mean and covariance matrix of input variables according to unscented transformation method.Secondly,the RBF neural network is used to solve the nonlinear equations of the power flow,and the mean of the output variables and the corresponding weights are obtained.The proposed method is suitable for the input variable with correlation.This ability improves the speed of algorithm due to not requiring calculating the Jacobian matrix and its inverse matrix.Finally,the unbalanced 25-bus system was tested in the simulation calculation.The results show the effectiveness and practicability of the proposed algorithm.关键词
三相概率潮流/RBF神经网络/无迹变换/相关性/雅可比矩阵Key words
three-phase probabilistic power flow/RBF neural network/unscented transformation/correlation/Jacobian matrix分类
信息技术与安全科学引用本文复制引用
周步祥,邓苏娟,张百甫..基于RBF神经网络和无迹变换法的三相概率潮流计算[J].电测与仪表,2018,55(11):7-11,18,6.
