Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Jinping Sun; Qifang Chen; Mingchao Xia

doi:10.17775/CSEEJPES.2020.06860

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Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Regular Papers | 更新时间：2026-10-15

- Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids
- Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第1期页码：261-271
- 作者机构：
  
  School of Electrical Engineering Beijing Jiaotong University (BJTU),Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.06860
  中图分类号：
- 纸质出版：2024-01-01
- 稿件说明：
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Jinping Sun, Qifang Chen, Mingchao Xia. Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):261-271.

Jinping Sun, Qifang Chen, Mingchao Xia. Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 261-271.
Jinping Sun, Qifang Chen, Mingchao Xia. Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):261-271. DOI： 10.17775/CSEEJPES.2020.06860.

Jinping Sun, Qifang Chen, Mingchao Xia. Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 261-271. DOI： 10.17775/CSEEJPES.2020.06860.

摘要

Abstract

Line parameters play an important role in the control and management of distribution systems. Currently

phasor measurement unit (PMU) systems and supervisory control and data acquisition (SCADA) systems coexist in distribution systems. Unfortunately

SCADA and PMU measurements usually do not match each other

resulting in inaccurate detection and identification of line parameters based on measurements. To solve this problem

a data-driven method is proposed. SCADA measurements are taken as samples and PMU measurements as the population. A probability parameter identification index (PPII) is derived to detect the whole line parameter based on the probability density function (PDF) parameters of the measurements. For parameter identification

a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation (KDE) and a conditional PDF. Then

the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings (MH) algorithm. Finally

using the power-loss samples and PMU current measurements

the line parameters are identified using the total least squares (TLS) algorithm. Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

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