Robust State Estimation Against Measurement and Network Parameter Errors

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 formul...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-09, Vol.33 (5), p.4751-4759
Main Authors: Lin, Yuzhang, Abur, Ali
Format: Article
Language:English
Subjects:
Computer simulation
Least absolute value
Linear programming
Measurement errors
Measurement uncertainty
Noise measurement
Parameter estimation
Parameter identification
Parameter modification
Parameter robustness
Power system modeling
power system modelling
Random noise
Robustness
Solvers
State estimation
Transmission line measurements
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2794331