电力系统自动化2016，Vol.40Issue(16)：38-43,82,7.DOI:10.7500/AEPS20151207006
基于脉冲神经网络伪量测建模的配电网三相状态估计
Three-phase State Estimation in Distribution Systems Based on Pseudo Measurement Modeling Using Spiking Neural Network
摘要
Abstract
To provide comprehensive and accurate real‐time data for distribution management system (DMS) , three‐phase state estimation in distribution system is critically required . Lacking in real‐time measurements in distribution system state estimation (DSSE) , this paper presents a spiking neural network ( SNN) based method for pseudo measurement modeling . In the proposed method , the pseudo measurements are firstly derived from a few real measurements using SNN . Then , the error associated with the generated pseudo measurements is created by Gaussian mixture model ( GMM ) . At last , the three‐phase state estimation in distribution system is made based on weighted least square method . The simulation results demonstrate the accurate performance of the proposed pseudo modeling in DSSE with both regards to normal situation and communication failure .关键词
配电网/状态估计/脉冲神经网络/高斯混合模型/伪量测Key words
distribution network/state estimation/spiking neural network/Gaussian mixture model/pseudo measurement引用本文复制引用
黄蔓云,孙国强,卫志农,臧海祥,陈通,陈胜..基于脉冲神经网络伪量测建模的配电网三相状态估计[J].电力系统自动化,2016,40(16):38-43,82,7.基金项目
国家自然科学基金资助项目（51277052）。 （）
