Robust power system state estimation using t-distribution noise model
In this paper, we propose an optimal robust state estimator using maximum likelihood optimization with the $t$ -distribution noise model. In robust statistics literature, the $t$ -distribution is used to model Gaussian and non-Gaussian statistics. The influence function, an analytical tool in robust...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137155 |
| _version_ | 1826116143247523840 |
|---|---|
| author | Chen, Tengpeng Sun, Lu Ling, Keck-Voon Ho, Weng Khuen |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chen, Tengpeng Sun, Lu Ling, Keck-Voon Ho, Weng Khuen |
| author_sort | Chen, Tengpeng |
| collection | NTU |
| description | In this paper, we propose an optimal robust state estimator using maximum likelihood optimization with the $t$ -distribution noise model. In robust statistics literature, the $t$ -distribution is used to model Gaussian and non-Gaussian statistics. The influence function, an analytical tool in robust statistics, is employed to obtain the solution to the resulting maximum likelihood estimation optimization problem, so that the proposed estimator can be implemented within the framework of traditional robust estimators. Numerical results obtained from simulations of the IEEE 14-bus system, IEEE 118-bus system, and experiment on a microgrid demonstrated the effectiveness and robustness of the proposed estimator. The proposed estimator could suppress the influence of outliers with smaller average mean-squared errors (AMSE) than the traditional robust estimators, such as quadratic–linear, square-root, Schweppe–Huber generalized-M, multiple-segment, and least absolute value estimators. A new approximate AMSE formula is also derived for the proposed estimator to predict and evaluate its precision. |
| first_indexed | 2024-10-01T04:06:31Z |
| format | Journal Article |
| id | ntu-10356/137155 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:31Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1371552020-03-04T08:52:19Z Robust power system state estimation using t-distribution noise model Chen, Tengpeng Sun, Lu Ling, Keck-Voon Ho, Weng Khuen School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Influence Function (IF) Maximum Likelihood Estimation (MLE) In this paper, we propose an optimal robust state estimator using maximum likelihood optimization with the $t$ -distribution noise model. In robust statistics literature, the $t$ -distribution is used to model Gaussian and non-Gaussian statistics. The influence function, an analytical tool in robust statistics, is employed to obtain the solution to the resulting maximum likelihood estimation optimization problem, so that the proposed estimator can be implemented within the framework of traditional robust estimators. Numerical results obtained from simulations of the IEEE 14-bus system, IEEE 118-bus system, and experiment on a microgrid demonstrated the effectiveness and robustness of the proposed estimator. The proposed estimator could suppress the influence of outliers with smaller average mean-squared errors (AMSE) than the traditional robust estimators, such as quadratic–linear, square-root, Schweppe–Huber generalized-M, multiple-segment, and least absolute value estimators. A new approximate AMSE formula is also derived for the proposed estimator to predict and evaluate its precision. NRF (Natl Research Foundation, S’pore) Accepted version 2020-03-04T02:44:49Z 2020-03-04T02:44:49Z 2019 Journal Article Chen, T., Sun, L., Ling, K.-V., & Ho, W. K. (2020). Robust power system state estimation using t-distribution noise model. IEEE Systems Journal, 14(1), 771-781. doi:10.1109/JSYST.2018.2890106 1932-8184 https://hdl.handle.net/10356/137155 10.1109/JSYST.2018.2890106 1 14 771 781 en 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.2018.2890106. 11 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Influence Function (IF) Maximum Likelihood Estimation (MLE) Chen, Tengpeng Sun, Lu Ling, Keck-Voon Ho, Weng Khuen Robust power system state estimation using t-distribution noise model |
| title | Robust power system state estimation using t-distribution noise model |
| title_full | Robust power system state estimation using t-distribution noise model |
| title_fullStr | Robust power system state estimation using t-distribution noise model |
| title_full_unstemmed | Robust power system state estimation using t-distribution noise model |
| title_short | Robust power system state estimation using t-distribution noise model |
| title_sort | robust power system state estimation using t distribution noise model |
| topic | DRNTU::Engineering::Electrical and electronic engineering Influence Function (IF) Maximum Likelihood Estimation (MLE) |
| url | https://hdl.handle.net/10356/137155 |
| work_keys_str_mv | AT chentengpeng robustpowersystemstateestimationusingtdistributionnoisemodel AT sunlu robustpowersystemstateestimationusingtdistributionnoisemodel AT lingkeckvoon robustpowersystemstateestimationusingtdistributionnoisemodel AT howengkhuen robustpowersystemstateestimationusingtdistributionnoisemodel |