摘要
实时准确的运行数据是实现主动配电网在线运行分析与智能化控制管理的基础。为了解决配电网实时量测不足带来的估计结果不理想的问题,依据通信领域的置信传播(BP)算法,提出一种基于Forney式因子图的主动配电网状态估计方法。考虑到具体用户量测的稀缺性及分布式电源运行时受气候影响的随机性,该方法首先通过历史负荷曲线获得先验分布,为配电网建立了统计学的计及光照辐射度及风速的Forney式因子图模型,然后利用BP算法全局推理变量节点及因子节点双向传递的本地置信度和状态信息,来获得各状态变量的边缘分布。通过对某地区11节点配电网系统和IEEE 33节点配电网系统进行仿真,表明了所述方法具有良好的实时性且在配电网实时量测不足的情况下也有较理想的估计结果。
Real-time and accurate operation data are the basis of online operation analysis and intelligent control management of active distribution network.In order to solve the problem that the estimation result of the distribution network is not ideal by the insufficient of real-time measurement,astate estimation method of active distribution network based on Forney-type factor graph is proposed according to the belief propagation（BP）algorithm in the communication field.Considering the measurement scarcity of specific users and the randomness of distributed generator under the influence of climate,apriori distribution is firstly obtained through the historical load curve to establish a statistical Forney-type factor graph model,in which the irradiance and wind speed for the distribution network are taken into account.Then,the BP algorithm is used to globally reason the bidirectional local confidence and state information of variable nodes and factor nodes to obtain the edge distribution of each state variable.Through the simulation of the 11-node distribution network system in a certain area and IEEE 33-node distribution network system,the results show that the proposed method has good real-time performance and better estimation results under the condition of insufficient real-time measurement.
作者
卢锦玲
李伟光
孙辰军
LU Jinling1 , LI Weiguang1 , SUN Chenjun2(1. School of Electrical ＆ Electronic Engineering, North China Electric Power University, Baoding 071003, China 2. State Grid Hebei Electric Power Supply Co. Ltd., Shijiazhuang 050022, Chin)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第6期40-46,97,共8页
Automation of Electric Power Systems
关键词
主动配电网
状态估计
因子图
置信传播
active distribution network
state estimation
factor graph
belief propagation
