基于RBF神经网络和无迹变换法的三相概率潮流计算

不平衡负荷、不对称网络参数以及可再生能源的接入,引入了三相概率潮流.文中提出了一种新的三相概率潮流计算方法,将RBF神经网络与无迹变换法结合求解三相概率潮流.首先根据无迹变换法,利用输入变量均值和协方差矩阵求出输入变量的Sigma向量以及相应权重.其次,采用RBF神经网络求解潮流非线性方程,得到输出变量的均值以及相应权重.所提方法适用于输入变量相关的情况,且无需求解雅可比矩阵及其逆矩阵,加快了计算速度.最后,在不平衡25节点系统上进行仿真,结果表明了所提算法的有效性和实用性.

Three-phase Probabilistic Power Flow Calculation Based on RBF Neural Network and Unscented Transformation Algorithm

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.