基于节点注入功率的配电网运行拓扑辨识
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摘要
配电网安全监控和数据采集系统(DSCADA)采集的遥信数据存在抖动和误动情况,导致基于遥信数据的配电网拓扑信息可靠性不高。通过微型同步相量量测单元(μPMU)多次采样的节点注入功率,构建基于节点注入功率量测的支路电压偏差的方差模型。采用Kruskal算法得到以支路电压偏差的方差为线路权重的最小生成树,实现对配电网拓扑运行结构的辨识。在此基础上,对比分析节点注入功率量测的采样次数以及网络复杂程度对拓扑辨识误差的影响。IEEE 33和IEEE 123节点系统仿真结果表明,该配电网运行拓扑辨识算法具有较好的可靠性和实用性。
Erroneous remote signal in distribution supervisory control and data acquisition( DSCADA) decreases the reliability of distribution netw ork topology of remote signaling data. With adopting multi-sampling of pow er injections by micro synchronous phasor measurement unit( μPM U),this paper constructs the variance model of the branch voltage deviation based on the measurement of nodal pow er injection. The Kruskal algorithm is used to obtain the minimum spanning tree w ith the variance of the branch voltage deviation as the line w eight to achieve the identification of operation topology structure of distribution netw ork,and then the influence of the sampling times of the pow er injection data and the complexity of the distribution netw ork on the identification errors is analyzed. The simulation results of IEEE 33 and IEEE 123 node system show that the algorithm has good reliability and practicability for the topology identification in distribution netw ork.
Erroneous remote signal in distribution supervisory control and data acquisition( DSCADA) decreases the reliability of distribution netw ork topology of remote signaling data. With adopting multi-sampling of pow er injections by micro synchronous phasor measurement unit( μPM U),this paper constructs the variance model of the branch voltage deviation based on the measurement of nodal pow er injection. The Kruskal algorithm is used to obtain the minimum spanning tree w ith the variance of the branch voltage deviation as the line w eight to achieve the identification of operation topology structure of distribution netw ork,and then the influence of the sampling times of the pow er injection data and the complexity of the distribution netw ork on the identification errors is analyzed. The simulation results of IEEE 33 and IEEE 123 node system show that the algorithm has good reliability and practicability for the topology identification in distribution netw ork.
引文
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[15]李志,章禹,暴英凯,等.兼顾均匀性的多目标配电网重构方法[J].电力系统保护与控制,2016,44(14):69-75.LI Zhi,ZHANG Yu,BAO Yingkai,et al.M ulti-objective distribution netw ork reconfiguration based on system homogeneity[J].Power System Protection and Control,2016,44(14):69-75.
[16]BOLOGNANI S,BOF N,MICHELOTTI D,et al.Identification of pow er distribution netw ork topology via voltage correlation analysis[C]//52ndIEEE Conference on Decision and Control.Florence:IEEE,2013:1659-1664.
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[18]DEKA D,BACKHAUS S,CHERTOVY M.Structure learning and statistical estimation in distribution netw orks-part II[EB/OL].[2017-04-01].https://arxiv.org/abs/1502.07820.
[19]李传健,刘前进.基于Multi-agent和粒子群引导最小生成树的配电网重构算法[J].电力系统保护与控制,2011,39(6):24-28,33.LI Chuanjian,LIU Qianjin.A distribution netw ork reconfiguration algorithm based on multi-agent system and minimum spanning tree led by particle sw arm optimization[J].Pow er System Protection and Control,2011,39(6):24-28,33.
[20]徐建军,沙力妮,张艳,等.一种新的最小生成树算法[J].电力系统保护与控制,2011,39(14):107-112.XU Jianjun,SHA Lini,ZHANG Yan,et al.A new algorithm for minimum spanning tree[J].Pow er System Protection and Control,2011,39(14):107-112.
[2]ESTRADA R,TOMASI C,SCHMIDLER S C,et al.Tree topology estimation[J].IEEE Transactions on Pattern Analysis and M achine Intelligence,2015,37(8):1688-1701.
[3]宋杰,谢海宁,杨增辉,等.基于多源异构数据挖掘的配电网故障信息统计分析[J].电力系统保护与控制,2016,44(3):141-147.SONG Jie,XIE Haining,YANG Zenghui,et al.Statistical analysis of the distribution fault based on multiple source and isomerism data mining technology[J].Pow er System Protection and Control,2016,44(3):141-147.
[4]王金丽,盛万兴,王金宇,等.中低压配电网统一数据采集与监控系统设计和实现[J].电力系统自动化,2012,36(18):72-76,81.WANG Jinli,SHENG Wanxing,WANG Jinyu,et al.Implementation of remote control encryption test softw are in the distribution netw ork terminal based on Open SLL[J].Automation of Electric Pow er Systems,2012,36(18):72-76,81.
[5]薛辉,贾清泉,王宁,等.基于PMU量测数据和SCADA数据融合的电力系统状态估计方法[J].电网技术,2008(14):44-49.XUE Hui,JIA Qingquan,WANG Ning,et al.A novel pow er system state estimation method based on merging PM U-measured data into SCADA data[J].Pow er System Technology,2008(14):44-49.
[6]ZHAO J,ZHANG Y,ZHANG P,et al.Development of a WAMS based test platform for pow er system real time transient stabilitydetection and control[J/OL].Protection and Control of M odern Pow er Systems,(2016-06-20)[2017-04-01].https://doi.org/10.1186/s41601-016-0013-1.
[7]马静,张俣妤,马伟,等.基于关联矩阵标记法与回路矩阵的电网拓扑分析[J].电力系统自动化,2014,38(12):74-80.M A Jing,ZHANG Wuyu,M A Wei,et al.Pow er netw ork topological analysis based on incidence matrix notation method and loop matrix[J].Automation of Electric Pow er Systems,2014,38(12):74-80.
[8]姚玉斌,叶爽利,吴志良,等.稀疏矩阵法网络拓扑分析[J].电力系统保护与控制,2011,39(23):1-5.YAO Yubin,YE Shuangli,WU Zhiliang,et al.An electromagnetic loop netw ork topology identification method based on bus-batch search[J].Pow er System Protection and Control,2011,39(23):1-5.
[9]马静,张俣妤,马伟,等.一种新型电网拓扑错误辨识方法[J].电力自动化设备,2016,36(1):82-88.M A Jing,ZHANG Wuyu,M A Wei,et al.Topology error identification of pow er grid[J].Electric Pow er Automation Equipment,2016,36(1):82-88.
[10]DEKA D,BACKHAUS S,CHERTKOV M.Structure learning and statistical estimation in distribution netw orks-part I[EB/OL].[2017-04-01].https://arxiv.org/abs/1501.04131.
[11]CAVRARO G,ARGHANDEH R,POOLLA K,et al.Data-driven approach for distribution netw ork topology detection[C]//2015IEEE Pow er&Energy Society General M eeting.Denver:IEEE,2015:1-5.
[12]DEKA D,BACKHAUS S,CHERTOVY M.Estimating distribution grid topologies:A graphical learning based approach[C]//2016 Power Systems Computation Conference(PSCC).Genoa:IEEE,2016:1-7.
[13]刘志文,朱增坤,周治国,等.一种基于FPGA+ARM架构的μPMU实现[J].电测与仪表,2015,52(22):8-13.LIU Zhiw en,ZHU Zengkun,ZHOU Zhiguo,et al.A design ofμPMU based on the structure of FPGA+ARM[J].Electrical M easurement and Instrumentation,2015,52(22):8-13.
[14]徐岩,郅静.基于改进自适应遗传算法的PMU优化配置[J].电力系统保护与控制,2015,43(2):55-62.XU Yan,ZHI Jing.Optimal PM U configuration based on improved adaptive genetic algorithm[J].Pow er System Protection and Control,2015,43(2):55-62.
[15]李志,章禹,暴英凯,等.兼顾均匀性的多目标配电网重构方法[J].电力系统保护与控制,2016,44(14):69-75.LI Zhi,ZHANG Yu,BAO Yingkai,et al.M ulti-objective distribution netw ork reconfiguration based on system homogeneity[J].Power System Protection and Control,2016,44(14):69-75.
[16]BOLOGNANI S,BOF N,MICHELOTTI D,et al.Identification of pow er distribution netw ork topology via voltage correlation analysis[C]//52ndIEEE Conference on Decision and Control.Florence:IEEE,2013:1659-1664.
[17]周治国,高文焘,刘文亮.基于μPMU的主动配电网故障定位方法研究[J].电网与清洁能源,2016,23(3):73-77.ZHOU Zhiguo,GAO Wentao,LIU Wenliang.Research on fault location based onμPM U for active distribution netw ork[J].Pow er System and Clean Energy,2016,23(3):73-77.
[18]DEKA D,BACKHAUS S,CHERTOVY M.Structure learning and statistical estimation in distribution netw orks-part II[EB/OL].[2017-04-01].https://arxiv.org/abs/1502.07820.
[19]李传健,刘前进.基于Multi-agent和粒子群引导最小生成树的配电网重构算法[J].电力系统保护与控制,2011,39(6):24-28,33.LI Chuanjian,LIU Qianjin.A distribution netw ork reconfiguration algorithm based on multi-agent system and minimum spanning tree led by particle sw arm optimization[J].Pow er System Protection and Control,2011,39(6):24-28,33.
[20]徐建军,沙力妮,张艳,等.一种新的最小生成树算法[J].电力系统保护与控制,2011,39(14):107-112.XU Jianjun,SHA Lini,ZHANG Yan,et al.A new algorithm for minimum spanning tree[J].Pow er System Protection and Control,2011,39(14):107-112.