融合时-频域多源数据的低压配网拓扑识别研究

金阳忻; 徐永进; 胡书红

doi:doi:10.19912/j.0254-0096.tynxb.2024-1044

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

融合时-频域多源数据的低压配网拓扑识别研究

更新时间：2025-12-17

- 融合时-频域多源数据的低压配网拓扑识别研究
- Issue 10, Pages: 487-500(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1044
  CLC： M72
- Published Online：31 October 2025，
  
  Published：31 October 2025
- 稿件说明：
移动端阅览
金阳忻, 徐永进, 胡书红. 融合时-频域多源数据的低压配网拓扑识别研究[J]. 2025, (10): 487-500.
DOI：

金阳忻, 徐永进, 胡书红. 融合时-频域多源数据的低压配网拓扑识别研究[J]. 2025, (10): 487-500. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1044.

摘要

结合400 V级低压配电网（以下简称配网）树状拓扑结构特点

利用电参量数据相关性分析了常用配网拓扑识别方法的基本原理及其缺陷。提出融合时-频域多源数据的低压配网拓扑识别方法

由两个环节组成：1）前馈环节

通过改进谱聚类算法逐层聚类包含时、频域维度的节点电压数据。为方便计算上级分支点对应的节点电压向量

藉由簇内节点集的变换群特征

设计了用于区分簇拓扑类型（辐射型或干线型）的有限域神经网络;2）反馈环节

在被前馈环节压缩的解空间内

基于有功功率平衡原理检验和修正可疑节点

该闭环识别框架可提升结果的准确性及对复杂拓扑的适应性。最后

以国网浙江公司3个具有典型拓扑的居民/工商业试点台区作为算例

对比几类常用配网拓扑识别方法

所提方法的优势得到验证。

Abstract

As the basis of transformer range lean management

the precision of distribution （for short of 400V distribution） user-branch-distribution transformer topology draws more attention. Based on the characteristics of distribution tree topology

the principles and defects of current distribution topology identification methods were analyzed with the correlation among electrical parameters. A novel distribution topology identification method based on time-frequency domain data fusion was proposed

which consists of two links： 1） Forward link. Spectrum clustering algorithm was modified to cluster node voltages including fundamental and harmonic domains level by level.Furthermore

in order to calculate the node voltage vector corresponding to the upper level topology branches

a Confined-field Neural Network was devised

which is able to discriminate the topology type （radiation or trunk） via cluster node transformation group features. 2） Backward link. In the solution space compressed by the forward link

the active power balance principle was utilized to check and correct the suspicious nodes. Such closed loop identification framework possesses promoted result precision and sophisticated topology applicability. Finally

3 residential/commercial and industrial pilot transformer range with typical topology in State Grid Zhejiang Company were selected as examples. The proposed method was paralleled with several current distribution topology identification methods

with its advantages verified.

关键词

Keywords

references

邵晨旭, 汤奕. 计及电压越限风险的含分布式光伏配电网三相载荷不平衡多目标优化方法[J]. 太阳能学报, 2023, 44(12): 53-64.

朱乾龙, 李大为, 陶骏. 计及负荷特性的超高层建筑配电网两阶段优化[J]. 太阳能学报, 2024, 45(5): 422-430.

罗钧腾, 章坚民, 陈耀军, 等. 融合已知相别和地址信息的低压用户计量表箱识别[J]. 电力系统自动化, 2021, 45(9): 115-121.

刘顺成, 何禹清, 乔学博, 等. 基于合作博弈的交直流配电网光储协调规划方法[J]. 太阳能学报, 2024, 45(1): 534-544.

CHEN H Y, WANG X J, LI Z H, et al.Distributed sensing and cooperative estimation/detection of ubiquitous power Internet of Things[J]. Protection and control of modern power systems, 2019, 4(2): 1-8.

黄毕尧, 张明, 李建岐, 等. 联合高低频电力线通信的中压配电网拓扑自动识别方法[J]. 高电压技术, 2021, 47(7): 2350-2358.

杨志淳, 沈煜, 杨帆, 等. 基于数据关联分析的低压配电网拓扑识别方法[J]. 电测与仪表, 2020, 57(18): 5-11, 35.

CAVRARO G, ARGHANDEH R.Power distribution network topology detection with time-series signature verification method[J]. IEEE transactions on power systems, 2018, 33(4): 3500-3509.

陈招安, 黄纯, 张志丹, 等. 基于T型灰色关联度和KNN算法的低压配电网台区拓扑识别方法[J]. 电力系统保护与控制, 2021, 49(1): 163-169.

崔雪原, 刘晟源, 金伟超, 等. 基于APAA和改进DBSCAN算法的户变关系及相位识别方法[J]. 电网技术, 2021, 45(8): 3034-3043.

OLIVIER F, SUTERA A, GEURTS P, et al.Phase identification of smart meters by clustering voltage measurements[C]//2018 Power Systems Computation Conference (PSCC). Dublin, Ireland, 2018: 1-8.

姚志力,王志新.计及风光不确定性的综合能源系统两层级协同优化配置方法[J].电网技术,2020,44(12):4521-4531.

管永明, 王刚, 骆凯波, 等. 基于极端不平衡学习的泛化低压异常箱表关系识别研究与应用[J]. 电子学报, 2021, 49(8): 1507-1514.

HOSSEINI Z S, KHODAEI A, PAASO A.Machine learning-enabled distribution network phase identification[J]. IEEE transactions on power systems, 2021, 36(2): 842-850.

李坤, 周来, 张勇军, 等. 基于量测数据质量的低压台区拓扑识别结果可信度评价[J]. 电力系统自动化, 2021, 45(17): 99-107.

洪文慧, 李钦豪, 张勇军, 等. 基于二次矩阵补全的低压配电网相序识别算法[J]. 电力自动化设备, 2022, 42(9): 133-138.

FENG R H, YUAN W Q, XIAO M, et al.Topology identification in distribution networks based on alternating optimisation[J]. IET generation, transmission & distribution, 2020, 14(24): 5851-5857.

罗钧腾, 章坚民, 姚力, 等. 基于电压和功率数据的低压用户相别识别优化建模与应用[J]. 电力系统自动化, 2021, 45(7): 123-131.

HE X, QIU R C, AI Q, et al.A hybrid framework for topology identification of distribution grid with renewables integration[J]. IEEE transactions on power systems, 2021, 36(2): 1493-1503.

张巍, 徐进, 刘家鹏. 基于分支有功功率的低压有源配电网拓扑辨识方法[J]. 电力系统自动化, 2022, 46(15): 85-95.

FARAJOLLAHI M, SHAHSAVARI A, MOHSENIAN-RAD H.Topology identification in distribution systems using line current sensors: an MILP approach[J]. IEEE transactions on smart grid, 2020, 11(2): 1159-1170.

Q/GDW 12180—2021, 智能物联电能表功能及软件规范[S].

JAGANNATH A, LOPATTO P, MIOLANE L.Statistical thresholds for tensor PCA[J]. The annals of applied probability, 2020, 30(4): 1910-1933.

廖志伟, 谢汛恺, 郑广昱, 等. 基于电压曲线形态特征聚类的低压台区相位辨识[J]. 电力系统自动化, 2023, 47(7): 142-149.

BANERJEE J S, CHAKRABORTY A, CHATTOPADHYAY A.A decision model for selecting best reliable relay queue for cooperative relaying in cooperative cognitive radio networks: the extent analysis based fuzzy AHP solution[J]. Wireless networks, 2021, 27(4): 2909-2930.

LENSEN A, XUE B, ZHANG M J.Genetic programming for evolving a front of interpretable models for data visualization[J]. IEEE transactions on cybernetics, 2021, 51(11): 5468-5482.

DL/T5587—2021, 配电自动化系统设计规程[S].

GB 50052—2009, 供配电系统设计规范[S].

ROCCHETTA R.Enhancing the resilience of critical infrastructures: statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics[J]. Renewable and sustainable energy reviews, 2022, 159: 112185.

唐启凡, 张玉龙, 何士豪, 等. 自适应多视角子空间聚类[J]. 西安交通大学学报, 2021, 55(5): 102-112.

莫宗坚, 蓝以中, 赵春来. 代数学-上[M]. 2版. 北京: 高等教育出版社, 2015: 95-98, 287-297.

金阳忻, 徐永进, 黄小琼. 基于卷积神经网络的电能表测得值置信区间研究[J]. 中国电机工程学报, 2021, 41(19): 6630-6641.

金阳忻, 徐永进. 电能表无线计量脉冲信号抗干扰算法研究[J]. 电网技术, 2021, 45(S2): 115-123.

耿丽丽, 牛保宁. 深度神经网络模型压缩综述[J]. 计算机科学与探索, 2020, 14(9): 1441-1455.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

⁰