Topology identification and joint parameter estimation of a distribution network considering bilateral measurement errors

被引：0
|
作者：
Wu L. [1 ]
Xu C. [1 ]
Qiu Z. [2 ]
Cheng T. [2 ]
Chen F. [2 ]
机构：
[1] Guangdong Power Grid Corp, Guangzhou
[2] Dongguan Power Supply Bureau, Guangdong Power Grid Co., Ltd., Dongguan
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2023年 / 51卷 / 16期
基金：
中国国家自然科学基金;
关键词：
bilateral measurement error; distribution network; equivalent model; parameter estimation; topology identification;
D O I：
10.19783/j.cnki.pspc.221694
中图分类号：
学科分类号：
摘要：
Distribution network parameter estimation and topology identification are the basis of network planning, operational analysis and security control. The traditional linear regression method has high requirements for measurement data error or noise data, and the estimation is accurate only when there is no noise. However, the actual input measurement values (such as voltage amplitude and phase angle) and output measurement values (such as active and reactive power) have noise data. For topology estimation, even if the measurement error is small, the regression method cannot get accurate topology. Given this, the basic model of distribution network parameter estimation is constructed first, then the influence of measurement error on line parameter estimation and topology identification is quantitatively analyzed. A line parameter estimation model considering the bilateral measurement error is established. It is difficult to analyze the model because of its non-convexity, the minimum Rayleigh entropy problem is obtained by equivalent transformation based on the Lagrange function. Finally, simulation analysis based on the IEEE 8-node system is carried out, and compared with traditional linear regression and the least squares method. This proves that the proposed method has good estimation accuracy even when the measurement error reaches 10%. © 2023 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码：149 / 157
页数：8
相关论文
共 34 条
  • [11] LUO Qun, LIU Chunyu, GU Qiang, Et al., A topology identification method of distribution network based on optimal matching loop power, Electrical Measurement & Instrumentation, 56, 19, pp. 1-6, (2019)
  • [12] XU Dongliang, ZHAO Jian, WANG Xiaoyu, Et al., Distribution network topology detection and identification based on a directed adjacency matrix, Power System Protection and Control, 49, 16, pp. 76-85, (2021)
  • [13] SHENG Wanxing, WU Ming, JI Yu, Et al., Key techniques and engineering practice of distributed renewable generation clusters integration, Proceedings of the CSEE, 39, 8, pp. 2175-2186, (2019)
  • [14] MACEDO L H, FRANCO J F, RIDER M J, Et al., Optimal operation of distribution networks considering energy storage devices, IEEE Transactions on Smart Grid, 6, 6, pp. 2825-2836, (2015)
  • [15] LIU Kai, LI Zhenhai, LU Li, Et al., Topology identification method of distribution station area based on cluster analysis, Power System Protection and Control, 50, 6, pp. 165-171, (2022)
  • [16] WANG Rining, WU Yi, WEI Haoming, Et al., Topology identification method for distribution network substation based on intelligent terminal signature, Power System Protection and Control, 49, 6, pp. 83-89, (2021)
  • [17] FARZIN H, FOTUHI-FIRUZABAD M, MOEINI AGHTAIE M., Role of outage management strategy in reliability performance of multi-microgrid distribution systems, IEEE Transactions on Power Systems, 33, 3, pp. 2359-2369, (2018)
  • [18] CHEN Zhao'an, HUANG Chun, ZHANG Zhidan, Et al., A topology identification method for low-voltage distribution network based on T-type grey correlation degree and KNN algorithm, Power System Protection and Control, 49, 1, pp. 163-169, (2021)
  • [19] WANG Xudong, WANG Gaomeng, LIN Jikeng, Et al., Method for parameter identification of LV distribution network based on AMI metering data, Electric Power, 52, 5, pp. 63-69, (2019)
  • [20] LIU Yuxiao, ZHANG Ning, KANG Chongqing, A review on data-driven analysis and optimization of power grid, Automation of Electric Power Systems, 42, 6, pp. 157-167, (2018)
1234