Robust State Estimation for Power Systems Using Quasi-Newton Method

Yu Bai; Wenling Li; Xiaoming Li

doi:10.1109/DDCLS52934.2021.9455656

Robust State Estimation for Power Systems Using Quasi-Newton Method

Yu Bai
, Wenling Li
, Xiaoming Li

School of Automation Science and Electrical Engineering

Beihang University
Shenzhen University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
Editors	Mingxuan Sun, Huaguang Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1328-1333
Number of pages	6
ISBN (Electronic)	9781665424233
DOIs	https://doi.org/10.1109/DDCLS52934.2021.9455656
State	Published - 14 May 2021
Event	10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 - Suzhou, China Duration: 14 May 2021 → 16 May 2021

Publication series

Name	Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

Conference

Conference	10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Country/Territory	China
City	Suzhou
Period	14/05/21 → 16/05/21

Keywords

Distributed algorithm
Maximum correntropy criterion
Power system state estimation
quasi-Newton methods

Access to Document

10.1109/DDCLS52934.2021.9455656

Cite this

Bai, Y., Li, W., & Li, X. (2021). Robust State Estimation for Power Systems Using Quasi-Newton Method. In M. Sun, & H. Zhang (Eds.), Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021 (pp. 1328-1333). Article 9455656 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS52934.2021.9455656

Bai, Yu ; Li, Wenling ; Li, Xiaoming. / Robust State Estimation for Power Systems Using Quasi-Newton Method. Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. editor / Mingxuan Sun ; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1328-1333 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021).

@inproceedings{f3d7ca16247e46bba93916f8d9e20dd5,

title = "Robust State Estimation for Power Systems Using Quasi-Newton Method",

abstract = "This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.",

keywords = "Distributed algorithm, Maximum correntropy criterion, Power system state estimation, quasi-Newton methods",

author = "Yu Bai and Wenling Li and Xiaoming Li",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; Conference date: 14-05-2021 Through 16-05-2021",

year = "2021",

month = may,

day = "14",

doi = "10.1109/DDCLS52934.2021.9455656",

language = "英语",

series = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bai, Y, Li, W & Li, X 2021, Robust State Estimation for Power Systems Using Quasi-Newton Method. in M Sun & H Zhang (eds), Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021., 9455656, Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1328-1333, 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021, Suzhou, China, 14/05/21. https://doi.org/10.1109/DDCLS52934.2021.9455656

Robust State Estimation for Power Systems Using Quasi-Newton Method. / Bai, Yu; Li, Wenling; Li, Xiaoming.
Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. ed. / Mingxuan Sun; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1328-1333 9455656 (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust State Estimation for Power Systems Using Quasi-Newton Method

AU - Bai, Yu

AU - Li, Wenling

AU - Li, Xiaoming

PY - 2021/5/14

Y1 - 2021/5/14

N2 - This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.

AB - This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.

KW - Distributed algorithm

KW - Maximum correntropy criterion

KW - Power system state estimation

KW - quasi-Newton methods

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T3 - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

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BT - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

A2 - Sun, Mingxuan

A2 - Zhang, Huaguang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

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ER -

Bai Y, Li W, Li X. Robust State Estimation for Power Systems Using Quasi-Newton Method. In Sun M, Zhang H, editors, Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1328-1333. 9455656. (Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021). doi: 10.1109/DDCLS52934.2021.9455656

Robust State Estimation for Power Systems Using Quasi-Newton Method

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