TY - JOUR
T1 - Robust State Estimation Against Measurement and Network Parameter Errors
AU - Lin, Yuzhang
AU - Abur, Ali
N1 - Funding Information:
Manuscript received March 16, 2017; revised July 23, 2017 and October 27, 2017; accepted January 12, 2018. Date of publication January 16, 2018; date of current version August 22, 2018. This work was supported in part by the Engineering Research Center Program of the National Science Foundation through Engineering Research Center, in part by the Department of Energy under NSF Award EEC-1041877, and in part by the CURENT Industry Partnership Program. Paper no. TPWRS-00378-2017. (Corresponding author: Ali Abur.) The authors are with the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115 USA (e-mail: yuzlin@ece. neu.edu; a.abur@neu.edu). Digital Object Identifier 10.1109/TPWRS.2018.2794331
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - 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.
AB - 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.
KW - Least absolute value
KW - parameter estimation
KW - power system modelling
KW - robustness
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=85041687214&partnerID=8YFLogxK
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U2 - 10.1109/TPWRS.2018.2794331
DO - 10.1109/TPWRS.2018.2794331
M3 - Article
AN - SCOPUS:85041687214
SN - 0885-8950
VL - 33
SP - 4751
EP - 4759
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 5
M1 - 8259469
ER -