摘要
分布式电源的广泛接入增加了配电网复杂程度,导致负荷模型的描述难度大幅提高。此外,针对网络每个节点分别进行建模以及对整个配电网进行建模的方法难以适用于不断扩大规模的配电网。提出一种基于社区划分的门控循环单元(GRU)神经网络负荷建模方法。通过加文-纽曼(GN)算法对配电网拓扑进行分区处理,基于GRU神经网络分别对各个社区进行负荷建模,并在网络输入特征向量中引入度中心性来衡量节点在网络拓扑中的重要程度。对某地区的10 kV配电网进行建模分析,结果表明所建模型精度及计算效率更高。
The extensive access of distributed power supplies increases the complexity of power distribution networks,resulting in the difficulty of the description by load models.In addition,modeling for the whole distribution network and each node in it is inapplicable for the ever-expanding distribution network.Therefore,a Gated Recurrent Unit(GRU)neural network load modeling method based on community division is proposed.Firstly,the GN algorithm is used to partition the topology of the distribution network.Then,load model for each community is constructed based on GRU neural network,in which degree centrality is introduced into the input feature vectors to measure the importance of nodes in the network topology.Finally,modeling analysis is carried out on a 10 kV distribution network.The results show that the proposed modelling mothed is of higher accuracy and computational efficiency.
作者
赵省军
张开鹏
付鑫权
司英莲
刘志栋
周登钰
ZHAO Shengjun;ZHANG Kaipeng;FU Xinquan;SI YingLian;LIU Zhidong;ZHOU Dengyu(State Grid Gansu Electric Power Company Wuwei Power Supply Company,Wuwei 733000,China;School of Electrical and Electronic Information,Xihua University,Chengdu 610039,China)
出处
《综合智慧能源》
CAS
2022年第2期15-20,共6页
Integrated Intelligent Energy
基金
国网甘肃省电力公司科技项目(52273018000L)。
关键词
分布式电源
负荷模型
社区划分
度中心性
GRU神经网络
distributed generation
load model
community division
degree centrality
GRU neural network
作者简介
赵省军(1982),男,高级工程师,从事新型电力系统构建、电力系统建设工作,zhaoxj@gs.sgcc.com.cn;张开鹏(1974),男,高级工程师,从事继电保护、农电管理、变电运行管理、调度管理工作,zhangkpww@gs-sgcc.com.cn;付鑫权(1997),男,在读硕士研究生,从事电力系统数据分析及负荷特性分析研究,745034344@qq.com;通信作者:刘志栋(1990),男,工程师,从事电网运行方式管理工作,867533440@qq.com。
