A Minimized Data Collection Optimization Method for Distribution Networks Considering Multiple-Time and Compressed Candidate Sets

doi:10.11930/j.issn.1004-9649.202307052

Abstract

Abstract:

At present, the insufficient observability of distribution networks leads to the lack of energy management ability of medium and low voltage distribution networks under large-scale distributed power grid connection. The minimized data collection technology for distribution networks can optimize the measurement configuration with the minimum economic cost, which plays an important role in improving the system observability. In this paper, a minimized data collection optimization method considering multiple time sections is proposed. The model of this method is solved in two stages: In the first stage, the candidate measurement set is compressed, with the Fisher information matrix (FIM) value used as the pheromone update parameter of ant colony algorithm. Since an iterative update is not required for the FIM, a significant decrease in algorithm complexity can be found. On this basis, the second stage involves a consideration of the accuracy requirement for state estimation, determining the optimal configuration scheme according to the ant colony algorithm. The comparison of schemes shows that the minimized data collection method in this paper, with a full consideration of the influence of power flow distribution changes on state estimation accuracy, realizes the intensive configuration and efficient calculation of data collection terminals, so as to ensure economical terminal investments and highly observable distribution networks.

Key words: distribution network, state estimation, minimized data collection, ant colony algorithm, Fisher information matrix (FIM), multiple-time

Zhaoxiang YUAN, Zhihong XIAO, Jing WANG, Yanling YU, Yan HUANG, Xingle GAO. A Minimized Data Collection Optimization Method for Distribution Networks Considering Multiple-Time and Compressed Candidate Sets[J]. Electric Power, 2023, 56(12): 20-30.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202307052

https://www.electricpower.com.cn/EN/Y2023/V56/I12/20

Figures/Tables 18

Fig.1 Two-stage measurement optimization configuration

Fig.2 Flowchart of minimized data collection optimization method for distribution networks

Fig.3 Schematic diagram of a 5-node test system

Table 1 Initial measurement configuration of a 5-node system

量测类型		量测布置位置
节点电压幅值量测		1
节点注入功率量测		2，3，4，5
支路功率量测		1—2，2—3，3—4，4—5

Table 2 State estimation accuracy with one-branch current amplitude measurements

方案编号	配置1处量测时的配置方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$I_{12}$	2.3212×10^–4
2	$I_{23}$	5.1805×10^–4
3	$I_{34}$	1.9589×10^–4
4	$I_{45}$	2.4112
本文优化方案	$I_{34}$	1.9589×10^–4

Table 2 State estimation accuracy with one-branch current amplitude measurements

方案编号	配置1处量测时的配置方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$I_{12}$	2.3212×10^–4
2	$I_{23}$	5.1805×10^–4
3	$I_{34}$	1.9589×10^–4
4	$I_{45}$	2.4112
本文优化方案	$I_{34}$	1.9589×10^–4

Table 3 State estimation accuracy with two-branch current amplitude measurements

方案编号	配置2个电流幅值量测时的方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$I_{12}, I_{23}$	5.2133×10^–4
2	$I_{12}, I_{34}$	1.9674 ×10^–4
3	$I_{12}, I_{45}$	2.4065 ×10^–4
4	$I_{23}, I_{34}$	4.9873×10^–4
5	$I_{23}, I_{45}$	5.0931×10^–4
6	$I_{34}, I_{45}$	1.7140 ×10^–4
本文优化方案	$I_{34}, I_{45}$	1.7140×10^–4

Table 3 State estimation accuracy with two-branch current amplitude measurements

方案编号	配置2个电流幅值量测时的方案	状态估计精度
0	未配置电流幅值量测	1.6602
1	$I_{12}, I_{23}$	5.2133×10^–4
2	$I_{12}, I_{34}$	1.9674 ×10^–4
3	$I_{12}, I_{45}$	2.4065 ×10^–4
4	$I_{23}, I_{34}$	4.9873×10^–4
5	$I_{23}, I_{45}$	5.0931×10^–4
6	$I_{34}, I_{45}$	1.7140 ×10^–4
本文优化方案	$I_{34}, I_{45}$	1.7140×10^–4

Table 4 Calculation results of different methods with one-branch current amplitude measurements

方案编号	文献[20]方法（Fisher信息矩阵，取最大值）	文献[19] 方法（状态估计误差，取最小值）
0	0.1656	0.04071
1	0.2495	0.04068
2	3.6769	0.04060
3	3.8064	0.04061
4	1.1723	0.04068

Table 5 Calculation results of different methods with two-branch current amplitude measurements

方案编号	文献[20]方法（Fisher信息矩阵，取最大值）	文献[19] 方法（状态估计误差，取最小值）
0	0.1656	0.04071
1	2.3101	0.04057
2	2.7738	0.04058
3	3.1701	0.04065
4	4.2601	0.04049
5	5.0398	0.04057
6	4.7315	0.04058

Fig.4 Connection diagram of a 33-node system

Table 6 Initial measurement sets of a 33-node system

量测类型		量测布置位置
节点电压幅值量测		1，2，3，4，5，6，7，8，9，10，11，12，13，14，15，16，17，18，19，20，21，22，23，24，25，26，27，28，29，30，31，32，33
节点注入功率量测		2，3，4，5，6，7，8，9，10，11，12，13，14，15，16，17，18，19，20，21，22，23，24，25，26，27，28，29，30，31，32，33
支路功率量测		1—2，2—3 ，3—4 ，4—5，5—6，6—7，7—8，8—9，9—10，10—11，11—12，12—13，13—14，14—15，15—16，16—17，17—18，19—20，20—21，21—22，3—23，23—24，24—25，6—26，26—27，27—28，28—29，29—30，30—31，31—32，32—33，2—19

Table 7 Candidate measurement sets of a 33-node system

候选量测集中量测类型		电流幅值量测布置位置
支路电流幅值量测		1—2，2—3 ，3—4 ，4—5，5—6，6—7，7—8，8—9，9—10，10—11，11—12，12—13，13—14，14—15，15—16，16—17，17—18，19—20，20—21，21—22，3—23，23—24，24—25，6—26，26—27，27—28，28—29，29—30，30—31，31—32，32—33，2—19

Fig.5 Calculation results of Stage I

Table 8 Candidate measurement set of the 33-node system updated by Stage-I calculation

候选量测集中量测类型		电流幅值量测布置位置
支路电流幅值量测		1—2，2—3，4—5，5—6，6—7，9—10，10—11，11—12，12—13，14—15，16—17，23—24，26—27，27—28，29—30， 31—32

Fig.6 Calculation results of Stage II

Fig.7 Calculation results of a single-stage algorithm

Table 9 Comparison of calculation results through different methods

方法	电流幅值量测位置	状态估计精度	耗时/min
单阶段算法	2—3，3—4，5—6，6—7	0.01377448	867.4580
本文算法	2—3，4—5，5—6，6—7	0.01377246	211.8633

Fig.8 Predicted value of load active power

Table 10 Result comparison of measurement configuration optimization on single / multiple time sections

断面	电流幅值量测位置	24个时刻状态估计精度累加值	耗时/min
单时间断面	1—2, 13—14, 26—27, 27—28	38.5945	4009.6000
多时间断面	1—2, 2—3, 3—4, 23—24	0.7854	2201.7027

References 28

1	张子桐, 周群, 佃钰林, 等. 基于μPMU测量数据的配电网线路阻抗参数估计[J]. 中国电力, 2023, 56 (8): 157- 165. DOI
	ZHANG Zitong, ZHOU Qun, DIAN Yulin, et al. Parameter estimation for line impedance in distribution network based on μPMU data[J]. Electric Power, 2023, 56 (8): 157- 165. DOI
2	程林, 万宇翔, 齐宁, 等. 含多种分布式资源的配用电系统运行可靠性研究评述及展望[J]. 电力系统自动化, 2021, 45 (22): 191- 207.
	CHENG Lin, WAN Yuxiang, QI Ning, et al. Review and prospect of research on operation reliability of power distribution and consumption system considering various distributed energy resources[J]. Automation of Electric Power Systems, 2021, 45 (22): 191- 207.
3	MONTICELLI A, WU F F. Network observability: identification of observable islands and measurement placement[J]. IEEE Power Engineering Review, 1985, PER-5 (5): 32.
4	GOU B, ABUR A. An improved measurement placement algorithm for network observability[J]. IEEE Transactions on Power Systems, 2001, 16 (4): 819- 824. DOI
5	GOU B, ABUR A. A direct numerical method for observability analysis[J]. IEEE Transactions on Power Systems, 2000, 15 (2): 625- 630. DOI
6	尹冠雄, 王彬, 孙宏斌, 等. 多场景适配的多能流在线状态估计功能研发与应用[J]. 中国电机工程学报, 2020, 40 (21): 6794- 6804.
	YIN Guanxiong, WANG Bin, SUN Hongbin, et al. Multi-scene adaptive online state estimation of multi-energy network: development and application[J]. Proceedings of the CSEE, 2020, 40 (21): 6794- 6804.
7	季宇, 孙彦萍, 吴鸣, 等. 基于可观测性分析的计及DG不确定性配电网表计优化配置[J]. 电力自动化设备, 2017, 37 (3): 26- 32.
	JI Yu, SUN Yanping, WU Ming, et al. Optimal meter allocation based on observability analysis with consideration of DG uncertainty for distribution network[J]. Electric Power Automation Equipment, 2017, 37 (3): 26- 32.
8	SCHWEPPE F C, WILDES J. Power system static-state estimation, part I: exact model[J]. IEEE Transactions on Power Apparatus and Systems, 1970, PAS-89 (1): 120- 125. DOI
9	徐航, 鞠力, 董树锋, 等. 提高配电网状态估计精度的智能配电单元优化布点方法[J]. 电网技术, 2018, 42 (4): 1210- 1216.
	XU Hang, JU Li, DONG Shufeng, et al. Optimization of intelligent distribution unit placement to improve accuracy of distribution network state estimation[J]. Power System Technology, 2018, 42 (4): 1210- 1216.
10	PRASAD S, VINOD KUMAR D M. Trade-offs in PMU and IED deployment for active distribution state estimation using multi-objective evolutionary algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67 (6): 1298- 1307. DOI
11	徐艳春, 刘晓明, 席磊, 等. 改进双因子抗差贝叶斯估计在区域配网状态估计中的性能分析[J]. 中国电机工程学报, 2021, 41 (14): 4879- 4890.
	XU Yanchun, LIU Xiaoming, XI Lei, et al. Performance analysis of improved two-factor robust Bayes estimation in state estimation of regional distribution network[J]. Proceedings of the CSEE, 2021, 41 (14): 4879- 4890.
12	徐俊俊, 戴桂木, 吴在军, 等. 计及电动汽车和光伏不确定性的主动配电网量测优化配置[J]. 电力系统自动化, 2017, 41 (1): 57- 64. DOI
	XU Junjun, DAI Guimu, WU Zaijun, et al. Optimal meter placement for active distribution network considering uncertainties of plug-in electric vehicles and photovoltaic systems[J]. Automation of Electric Power Systems, 2017, 41 (1): 57- 64. DOI
13	王克英, 穆钢, 陈学允. 计及PMU的状态估计精度分析及配置研究[J]. 中国电机工程学报, 2001, 21 (8): 29- 33. DOI
	WANG Keying, MU Gang, CHEN Xueyun. Precision improvement and pmu placement studies on state estimation of a hybrid measurement system with pmus[J]. Proceedings of the CSEE, 2001, 21 (8): 29- 33. DOI
14	高亚静, 张占龙, 吴文传, 等. 配电网量测配置评估及优化[J]. 中国电力, 2014, 47 (7): 39- 44.
	GAO Yajing, ZHANG Zhanlong, WU Wenchuan, et al. Evaluation and optimization of measurement configuration in distribution system[J]. Electric Power, 2014, 47 (7): 39- 44.
15	徐臣, 余贻鑫. 提高配电网状态估计精度的量测配置优化方法[J]. 电力自动化设备, 2009, 29 (7): 17- 21.
	XU Chen, YU Yixin. Evaluation and optimization of meter placement to enhance distribution state estimation[J]. Electric Power Automation Equipment, 2009, 29 (7): 17- 21.
16	KEKATOS V, GIANNAKIS G B, WOLLENBERG B. Optimal placement of phasor measurement units via convex relaxation[J]. IEEE Transactions on Power Systems, 2012, 27 (3): 1521- 1530. DOI
17	LI Q, CUI T, WENG Y, et al. An information-theoretic approach to PMU placement in electric power systems[J]. IEEE Transactions on Smart Grid, 2013, 4 (1): 446- 456. DOI
18	赵媛媛, 袁澎, 艾芊, 等. 考虑拓扑约束并采用改进遗传算法的PMU优化配置[J]. 电网技术, 2014, 38 (8): 2063- 2070.
	ZHAO Yuanyuan, YUAN Peng, AI Qian, et al. Improved genetic algorithm based optimal configuration of PMUs considering topological constraints[J]. Power System Technology, 2014, 38 (8): 2063- 2070.
19	CHEN X S, LIN J, WAN C, et al. Optimal meter placement for distribution network state estimation: a circuit representation based MILP approach[J]. IEEE Transactions on Power Systems, 2016, 31 (6): 4357- 4370. DOI
20	XYGKIS T C, KORRES G N, MANOUSAKIS N M. Fisher information-based meter placement in distribution grids via the D-optimal experimental design[J]. IEEE Transactions on Smart Grid, 2018, 9 (2): 1452- 1461. DOI
21	LIU J Q, PONCI F, MONTI A, et al. Optimal meter placement for robust measurement systems in active distribution grids[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63 (5): 1096- 1105. DOI
22	AMINIFAR F, KHODAEI A, FOTUHI-FIRUZABAD M, et al. Contingency-constrained PMU placement in power networks[J]. IEEE Transactions on Power Systems, 2010, 25 (1): 516- 523. DOI
23	张健磊, 高湛军, 王志远, 等. 基于有限μPMU的主动配电网故障定位方法[J]. 电网技术, 2020, 44 (7): 2722- 2731.
	ZHANG Jianlei, GAO Zhanjun, WANG Zhiyuan, et al. Fault location method for active distribution based on finite μPMU[J]. Power System Technology, 2020, 44 (7): 2722- 2731.
24	COSER J, COSTA A S, ROLIM J G. Metering scheme optimization with emphasis on ensuring bad-data processing capability[J]. IEEE Transactions on Power Systems, 2006, 21 (4): 1903- 1911. DOI
25	何胜, 杨斌, 俞明, 等. 一种谐波量测点优化配置的两阶段算法[J]. 电力系统及其自动化学报, 2021, 33 (6): 22- 27. DOI
	HE Sheng, YANG Bin, YU Ming, et al. Two-stage algorithm for optimal configuration of harmonic measurement points[J]. Proceedings of the CSU-EPSA, 2021, 33 (6): 22- 27. DOI
26	孙国城, 赵祖康. 地区电网状态估计研究[J]. 电力系统自动化, 1993, 17 (12): 41- 45. DOI
	SUN Guocheng, ZHAO Zukang. State estimation research on electrical distribution system[J]. Automation of Electric Power Systems, 1993, 17 (12): 41- 45. DOI
27	SEBERRY J , YAMADA M . More on maximal determinant matrices[M]. Hadamard Matrices: Constructions using Number Theory and Linear Algebra. Wiley: 245–269.
28	BU F K, YUAN Y X, WANG Z Y, et al. A time-series distribution test system based on real utility data[C]//2019 North American Power Symposium (NAPS). Wichita, KS, USA. IEEE, 2020: 1–6.

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