Covariance analysis of LAV robust dynamic state estimation in power systems
In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the covarianc...
Saved in:
Main Authors: | , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/141874 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-141874 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1418742021-01-06T02:31:50Z Covariance analysis of LAV robust dynamic state estimation in power systems Sun, Lu Chen, Tengpeng Ho, Weng Khuen Ling, Keck Voon Maciejowski, Jan M. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Dynamic State Estimation Phasor Measurement Unit In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the covariance of the robust Least Absolute Value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus and 118-bus systems are given to illustrate the usefulness of the equation. Monte-Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this paper is four-order of magnitude faster than a 10,000-run Monte-Carlo experiment on both the IEEE 14-bus and 30-bus systems. Accepted version 2020-06-11T06:35:46Z 2020-06-11T06:35:46Z 2019 Journal Article Sun, L., Chen, T., Ho, W. K., Ling, K. V., & Maciejowski, J. M. (2019). Covariance analysis of LAV robust dynamic state estimation in power systems. IEEE Systems Journal, 14(2) 2801-2812. doi:10.1109/JSYST.2019.2936595 1932-8184 https://hdl.handle.net/10356/141874 10.1109/JSYST.2019.2936595 2 14 2801 2812 en IEEE Systems Journal IEEE Systems Journal © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JSYST.2019.2936595 13 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering Dynamic State Estimation Phasor Measurement Unit |
spellingShingle |
Engineering::Electrical and electronic engineering Dynamic State Estimation Phasor Measurement Unit Sun, Lu Chen, Tengpeng Ho, Weng Khuen Ling, Keck Voon Maciejowski, Jan M. Covariance analysis of LAV robust dynamic state estimation in power systems |
description |
In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the
covariance of the robust Least Absolute Value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus and 118-bus systems are given to illustrate the usefulness of the equation. Monte-Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this paper is four-order of magnitude faster than a 10,000-run Monte-Carlo experiment on both the IEEE 14-bus and 30-bus systems. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Sun, Lu Chen, Tengpeng Ho, Weng Khuen Ling, Keck Voon Maciejowski, Jan M. |
format |
Article |
author |
Sun, Lu Chen, Tengpeng Ho, Weng Khuen Ling, Keck Voon Maciejowski, Jan M. |
author_sort |
Sun, Lu |
title |
Covariance analysis of LAV robust dynamic state estimation in power systems |
title_short |
Covariance analysis of LAV robust dynamic state estimation in power systems |
title_full |
Covariance analysis of LAV robust dynamic state estimation in power systems |
title_fullStr |
Covariance analysis of LAV robust dynamic state estimation in power systems |
title_full_unstemmed |
Covariance analysis of LAV robust dynamic state estimation in power systems |
title_sort |
covariance analysis of lav robust dynamic state estimation in power systems |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/141874 |
_version_ |
1688665268610400256 |