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A Distributed Robust System-Wide State Estimation Method for Power Systems Based on Maximum Correntropy

Authors :
Chen, Tengpeng
Ren, He
Chen, Guipeng
Gooi, Hoay Beng
Amaratunga, Gehan A. J.
Source :
IEEE Transactions on Industrial Informatics; December 2023, Vol. 19 Issue: 12 p11455-11465, 11p
Publication Year :
2023

Abstract

The distribution of measurement noise applied in practical power system state estimation (PSSE) can deviate from the assumed Gaussian model, and the performance of an estimator becomes bad if the Gaussian model is still used. This article proposes a new distributed robust PSSE method for multiarea power systems. The non-Gaussian model is utilized to fit the measurement noise distribution to reach high model accuracy. The proposed distributed method is derived based on the maximum correntropy criterion to further reduce the impact of non-Gaussian measurement noise and outliers. The influence function combined with the finite-time average consensus algorithm is used to implement the proposed distributed robust method in a fully distributed manner. Simulations conducted on the IEEE 30-bus, 118-bus and 300-bus systems and the Polish 2383-bus system demonstrate the robustness and effectiveness of the proposed distributed method. Each local area can get the system-wide robust state estimation solution by only using local information and small amounts of data from neighboring areas. Our proposed distributed robust method has at least 12% improvement in reducing the mean squared error under Gaussian-Uniform noise. It is verified the communication network applied for the proposed method is fairly flexible while the existing distributed approaches use a fixed communication network when the power systems are partitioned. The simulation results also verify the robustness of the proposed method to bad data and communication failure.

Details

Language :
English
ISSN :
15513203
Volume :
19
Issue :
12
Database :
Supplemental Index
Journal :
IEEE Transactions on Industrial Informatics
Publication Type :
Periodical
Accession number :
ejs64344669
Full Text :
https://doi.org/10.1109/TII.2023.3246465