.png){kind=icon}
.png){kind=icon}
Return
A Distributed Robust Power System State Estimation Approach Using t-Distribution Noise Model
.png){kind=icon}
PRE
AI
DOI：10.1109/JSYST.2020.2987612.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In practical power system applications, the distribution of the measurement noise is sometimes unknown and deviates from the assumed Gaussian noise model due to measurement outliers. In such cases, the performances of the state estimators based on Gaussian noise assumption may deteriorate significantly. In this article, we propose a fully distributed and robust power system state estimation approach based on the t-distribution noise model and the maximum likelihood criterion. The t-distribution is used to model Gaussian and non-Gaussian statistics in the field of robust statistics. The matrix-splitting techniques are employed to carry out the extensive matrix inversion of the gain matrix in a distributed way to achieve efficient computation. In the proposed distributed estimation framework, each local control area only requires limited data exchange with its neighboring areas. Simulations on the IEEE 14-bus, 118-bus and 300-bus systems are used to verify the effectiveness and robustness of the proposed distributed state estimation algorithm.
Keywords:
Distributed state estimation
maximum likelihood estimation
matrix-splitting
t-distribution
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
2.4
Papers:
4.5K
Citations:
387