A new robust dynamic state estimation approach for power systems with non-Gaussian noise

A new robust dynamic state estimation approach for power systems with non-Gaussian noise

The Gaussian noise distribution is typically used in dynamic state estimation (DSE) but it is not always true in practice because of abnormal system inputs, impulsive noise and measurement outliers. In this paper, a new robust DSE approach based on a new robust Lp norm based estimator and the cubatu...

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Main Authors: Chen, Tengpeng, Liu, Fangyan, Luo, Hongxuan, Foo, Eddy Yi Shyh, Sun, Lu, Sun, Yuhao, Gooi, Hoay Beng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Engineering
Non-Gaussian noise
Cubature Kalman filter
Online Access:https://hdl.handle.net/10356/178654
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1786542024-07-05T15:39:32Z A new robust dynamic state estimation approach for power systems with non-Gaussian noise Chen, Tengpeng Liu, Fangyan Luo, Hongxuan Foo, Eddy Yi Shyh Sun, Lu Sun, Yuhao Gooi, Hoay Beng School of Electrical and Electronic Engineering Engineering Non-Gaussian noise Cubature Kalman filter The Gaussian noise distribution is typically used in dynamic state estimation (DSE) but it is not always true in practice because of abnormal system inputs, impulsive noise and measurement outliers. In this paper, a new robust DSE approach based on a new robust Lp norm based estimator and the cubature Kalman filter (CKF) is developed for power systems with non-Gaussian noise statistics. The Lp norm based estimator is derived from the Lp norm formula and the quadratic formula in order to alleviate the impacts from bad data and outliers. The proposed Lp-CKF DSE approach exhibits good accuracy because a new estimation error covariance is obtained by using the influence function. The robustness of the proposed Lp-CKF DSE approach is verified by performing simulations on a generator in the IEEE 39-bus system. Published version This work was supported in part by the Natural Science Foundation of Fujian Province, China under Grant 2023H0004, in part by the Guangdong Basic and Applied Basic Research Foundation, China under Grant 2023A1515011319, in part by the Fundamental Research Funds for the Central Universities, China under Grant 20720230034 and 20720220084, and in part by the National Natural Science Foundation of China under Grant 61903314. 2024-07-02T04:21:32Z 2024-07-02T04:21:32Z 2024 Journal Article Chen, T., Liu, F., Luo, H., Foo, E. Y. S., Sun, L., Sun, Y. & Gooi, H. B. (2024). A new robust dynamic state estimation approach for power systems with non-Gaussian noise. International Journal of Electrical Power and Energy Systems, 158, 109948-. https://dx.doi.org/10.1016/j.ijepes.2024.109948 0142-0615 https://hdl.handle.net/10356/178654 10.1016/j.ijepes.2024.109948 2-s2.0-85188686627 158 109948 en International Journal of Electrical Power and Energy Systems © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). 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
Non-Gaussian noise
Cubature Kalman filter
spellingShingle Engineering
Non-Gaussian noise
Cubature Kalman filter
Chen, Tengpeng
Liu, Fangyan
Luo, Hongxuan
Foo, Eddy Yi Shyh
Sun, Lu
Sun, Yuhao
Gooi, Hoay Beng
A new robust dynamic state estimation approach for power systems with non-Gaussian noise
description The Gaussian noise distribution is typically used in dynamic state estimation (DSE) but it is not always true in practice because of abnormal system inputs, impulsive noise and measurement outliers. In this paper, a new robust DSE approach based on a new robust Lp norm based estimator and the cubature Kalman filter (CKF) is developed for power systems with non-Gaussian noise statistics. The Lp norm based estimator is derived from the Lp norm formula and the quadratic formula in order to alleviate the impacts from bad data and outliers. The proposed Lp-CKF DSE approach exhibits good accuracy because a new estimation error covariance is obtained by using the influence function. The robustness of the proposed Lp-CKF DSE approach is verified by performing simulations on a generator in the IEEE 39-bus system.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Chen, Tengpeng
Liu, Fangyan
Luo, Hongxuan
Foo, Eddy Yi Shyh
Sun, Lu
Sun, Yuhao
Gooi, Hoay Beng
format Article
author Chen, Tengpeng
Liu, Fangyan
Luo, Hongxuan
Foo, Eddy Yi Shyh
Sun, Lu
Sun, Yuhao
Gooi, Hoay Beng
author_sort Chen, Tengpeng
title A new robust dynamic state estimation approach for power systems with non-Gaussian noise
title_short A new robust dynamic state estimation approach for power systems with non-Gaussian noise
title_full A new robust dynamic state estimation approach for power systems with non-Gaussian noise
title_fullStr A new robust dynamic state estimation approach for power systems with non-Gaussian noise
title_full_unstemmed A new robust dynamic state estimation approach for power systems with non-Gaussian noise
title_sort new robust dynamic state estimation approach for power systems with non-gaussian noise
publishDate 2024
url https://hdl.handle.net/10356/178654
_version_ 1806059813460770816

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