.png){kind=icon}
Return
Robust State Estimation Against Measurement and Network Parameter Errors
.png){kind=icon}
OA
AI
DOI：10.1109/TPWRS.2018.2794331.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Errors in network parameters can cause serious bias in state estimation solution and make good measurements identified and discarded as bad data. This paper addresses this issue by developing a state estimator that remains robust against parameter errors. This is accomplished by modifying the formulation of the well-known least absolute value state estimator in order to identify and reject not only gross measurement errors, but also parameter errors in the network model. The resulting state estimate will not only be free from the impact of erroneous parameters, but will also reliably estimate and correct the erroneous parameters at the same time. The proposed approach can be formulated as a linear programming (LP) problem, and solved in a computational efficient manner by existing LP solvers. Simulation results show that it is effective under different types of parameter errors, gross measurement errors, and Gaussian noise.
Keywords:
Least absolute value
robustness
parameter estimation
power system modelling
state estimation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.2
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
Improving low fruit and vegetable intake in children: Findings from a system dynamics, community group model building study.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more