Power Distribution System Synchrophasor Measurements with Non-Gaussian Noises: Real-World Data Testing and Analysis

被引：13

作者：

Huang C. ^{[1
]}

Thimmisetty C. ^{[2
]}

Chen X. ^{[1
]}

Stewart E. ^{[3
]}

Top P. ^{[1
]}

Korkali M. ^{[1
]}

Donde V. ^{[1
]}

Tong C. ^{[1
]}

Min L. ^{[4
]}

机构：

[1] Lawrence Livermore National Laboratory, Livermore, 94550, CA

[2] Palo Alto Networks, Santa Clara, 95054, CA

[3] National Rural Electric Cooperative Association, Arlington, 22203, VA

[4] Precourt Institute for Energy, Stanford University, Stanford, 94305, CA

来源：

IEEE Open Access Journal of Power and Energy | 2021年 / 8卷

关键词：

Gaussian mixture model (GMM); measurement error; micro-PMU (μPMU); phasor measurement unit (PMU); Power distribution system; state estimation; synchrophasor;

D O I：

10.1109/OAJPE.2021.3081503

中图分类号：

学科分类号：

摘要：

This short paper investigates distribution-level synchrophasor measurement errors with online and offline tests, and mathematically and systematically identifies the actual distribution of the measurement errors through graphical and numerical analysis. It is observed that the measurement errors in both online and offline case studies follow a non-Gaussian distribution, instead of the traditionally assumed Gaussian distribution. It suggests the use of non-Gaussian models, such as Gaussian mixture models, for representing the measurement errors more accurately and realistically. The presented tests and analysis are helpful for the understanding of distribution-level measurement characteristics, and for the modeling and simulation of distribution system applications, such as state estimation. © 2020 IEEE.

引用

页码：223 / 228

页数：5

共 3 条

[1] ANALYSIS OF BAD DATA IN POWER SYSTEM STATE ESTIMATION UNDER NON-GAUSSIAN MEASUREMENT NOISE
Martinez-Parrales, R.
Fuerte-Esquivel, C. R.
Alcaide-Moreno, B. A.
ELECTRIC POWER SYSTEMS RESEARCH, 2020, 186
[2] Bayesian Distribution System State Estimation in presence of non-Gaussian Pseudo-Measurements
Angioni, Andrea
Pau, Marco
Ponci, Ferdinanda
Monti, Antonello
Muscas, Carlo
Sulis, Sara
Pegoraro, Paolo Attilio
2016 IEEE INTERNATIONAL WORKSHOP ON APPLIED MEASUREMENTS FOR POWER SYSTEMS (AMPS), 2016, : 1 - 6
[3] A Stochastic Event-Triggered Robust Cubature Kalman Filtering Approach to Power System Dynamic State Estimation With Non-Gaussian Measurement Noises
Li, Zhen
Li, Sen
Liu, Bin
Yu, Samson S.
Shi, Peng
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2023, 31 (02) : 889 - 896

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