LOW VOLTAGE DISTRIBUTION TOPOLOGY IDENTIFICATION METHOD BASED ON TIME-FREQUENCY DOMAIN MULTI-SOURCE DATA FUSION

Jin Yangxin; Xu Yongjin; Hu Shuhong

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

PDF(2053 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 487-500. DOI: 10.19912/j.0254-0096.tynxb.2024-1044

LOW VOLTAGE DISTRIBUTION TOPOLOGY IDENTIFICATION METHOD BASED ON TIME-FREQUENCY DOMAIN MULTI-SOURCE DATA FUSION

Jin Yangxin, Xu Yongjin, Hu Shuhong

Author information +

History +

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.

Key words

distribution topology identification / spectrum clustering / node set transformation group / time-frequency domain multi-source data / finite domain neural network / closed loop identification framework

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Jin Yangxin, Xu Yongjin, Hu Shuhong. LOW VOLTAGE DISTRIBUTION TOPOLOGY IDENTIFICATION METHOD BASED ON TIME-FREQUENCY DOMAIN MULTI-SOURCE DATA FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 487-500 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1044

References

[1] 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.
SHAO C X, TANG Y.Imbalance multi-objective optimization method of three-phase laod for distribued photovoltaic power distribution net work considering risk of voltage overrun[J]. Acta energiae solaris sinica,2023, 44(12): 53-64.
[2] 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.
ZHU Q L, LI D W, TAO J.Two-stage optimization of distribution network for super high-rise buildings considering load characteristics[J]. Acta energiae solaris sinica, 2024, 45(5): 422-430.
[3] 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.
LUO J T, ZHANG J M, CHEN Y J, et al.Identification of low-voltage user meter box combined with known phase and address information[J]. Automation of electric power systems, 2021, 45(9): 115-121.
[4] 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.
LIU S C, HE Y Q, QIAO X B, et al.Photovoltaic-storage coordinated planning method of AC/DC distribution network based on cooperative game[J]. Acta energiae solaris sinica, 2024, 45(1): 534-544.
[5] 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.
[6] 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.
HUANG B Y, ZHANG M, LI J Q, et al.Automatic identification of medium-voltage power distribution network topology based on high and low frequency power line communication[J]. High voltage engineering, 2021, 47(7): 2350-2358.
[7] 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.
YANG Z C, SHEN Y, YANG F, et al.Topology identification method of low voltage distribution network based on data association analysis[J]. Electrical measurement & instrumentation, 2020, 57(18): 5-11, 35.
[8] 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.
[9] 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.
CHEN Z A, HUANG C, ZHANG Z D, et al.Topology identification method of a low voltage distribution network based on T-type grey correlation degree and KNN algorithm[J]. Power system protection and control, 2021, 49(1): 163-169.
[10] 张然, 孙晓璐, 何仲潇, 等. 基于异常点检测和改进K-means算法的台区用户相别辨识方法[J]. 智慧电力, 2020, 48(1): 91-96.
ZHANG R, SUN X L, HE Z X, et al.Phase identification method for distribution area users based on outlier detection and improved K-means algorithm[J]. Smart Power, 2020, 48(1): 91-96.
[11] 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.
CUI X Y, LIU S Y, JIN W C, et al.Consumer-transformer relationship and phase identification based on APAA and improved DBSCAN algorithm[J]. Power system technology, 2021, 45(8): 3034-3043.
[12] 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.
[13] 连子宽, 姚力, 刘晟源, 等. 基于t-SNE降维和BIRCH聚类的单相用户相位及表箱辨识[J]. 电力系统自动化, 2020, 44(8): 176-184.
LIAN Z K, YAO L, LIU S Y, et al.Phase and meter box identification for single-phase users based on t-SNE dimension reduction and BIRCH clustering[J]. Automation of electric power systems, 2020, 44(8): 176-184.
[14] 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.
YAO Z L,WANG Z X.Two-level collaborative optimal allocation method of integrated energy system considering wind and solar uncertainty[J]. Power system technology, 2020, 44(12): 4521-4531.
[15] 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.
GUAN Y M, WANG G, LUO K B, et al.Generalized identification for low-pressure abnormal box-table relationship based on extreme unbalance classification learning[J]. Acta electronica sinica, 2021, 49(8): 1507-1514.
[16] 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.
[17] 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.
LI K,ZHOU L,ZHANG Y J, et al.Credibility evaluation of topology identification results in low-voltage distribution network based on quality of measured data[J]. Automation of electric power systems,2021,45(17):99-107.
[18] 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.
HONG W H, LI Q H, ZHANG Y J, et al.Quadratic matrix completion based phase sequence identification algorithm for low-voltage distribution network[J]. Electric power automation equipment, 2022, 42(9): 133-138.
[19] 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.
[20] 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.
LUO J T, ZHANG J M, YAO L, et al.Modeling and application of phase identification optimization for low-voltage customer based on voltage and power data[J]. Automation of electric power systems, 2021, 45(7): 123-131.
[21] 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.
[22] 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.
ZHANG W, XU J, LIU J P.Topology identification method for low-voltage active distribution network based on branch active power[J]. Automation of electric power systems, 2022, 46(15): 85-95.
[23] 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.
[24] Q/GDW 12180—2021, 智能物联电能表功能及软件规范[S].
Q/GDW 12180—2021, Function and software specifications for smart loT electricity meters[S].
[25] JAGANNATH A, LOPATTO P, MIOLANE L.Statistical thresholds for tensor PCA[J]. The annals of applied probability, 2020, 30(4): 1910-1933.
[26] 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.
LIAO Z W, XIE X K, ZHENG G Y, et al.Phase identification of low-voltage distribution station area based on morphological characteristic clustering of voltage curves[J]. Automation of electric power systems, 2023, 47(7): 142-149.
[27] 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.
[28] 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.
[29] DL/T5587—2021, 配电自动化系统设计规程[S].
DL/T5587—2021, Code for design of distribution automation system[S].
[30] GB 50052—2009, 供配电系统设计规范[S].
GB 50053—2009, Code for design electric power supply systems[S].
[31] 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.
[32] 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.
TANG Q F, ZHANG Y L, HE Z H, et al.Adaptive multi-view subspace clustering[J]. Journal of Xi’an Jiaotong University, 2021, 55(5): 102-112.
[33] 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.
MO Z J, LAN Y Z, ZHAO C L.Algebra - Volume I [M]. 2nd edition Beijing: Higher Education Press, 2015: 95-98, 287-297.
[34] 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.
JIN Y X, XU Y J, HUANG X Q.Confidence coefficient calculation model of the energy meter measurement based on convolutional neural network[J]. Proceedings of the CSEE, 2021, 41(19): 6630-6641.
[35] 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.
JIN Y X, XU Y J.Anti interference algorithm for wireless metering pulse signal of electric energy meter based on convolutional neural network[J]. Power system technology, 2021, 45(S2): 115-123.
[36] 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.
GENG L L, NIU B N.Survey of deep neural networks model compression[J]. Journal of frontiers of computer science and technology, 2020, 14(9): 1441-1455.