Power system state estimation based on Iterative Extended Kalman Filtering and bad data detection using normalized residual test

Author

Alamin, Abubeker ; Khalid, Haris M. ; Peng, Jimmy C.-H

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., Inst. Center for Energy, Abu Dhabi, United Arab Emirates

fYear

2015

fDate

20-21 Feb. 2015

Firstpage

1

Lastpage

5

Abstract

This paper proposed an enhanced real-time state estimation using Iterative Extended Kaiman Filtering (IEKF). The IEKF estimated state variables based on past state variables. Largest Normalized Residual Test (LNRT) was integrated with IEKF for bad data detection. A comparison with the conventional Weighted Least Squares (WLS) was also investigated using the IEEE 14 bus test system simulated in MATLAB. Based on the results, the merits and limitations of IEKF were summarized.

Keywords

Kalman filters; iterative methods; nonlinear filters; power system state estimation; statistical testing; IEEE 14 bus test system; IEKF; LNRT; MATLAB; bad data detection; iterative extended Kalman filtering; largest normalized residual test; power system state estimation; real-time state estimation enhancement; state variables; Kalman filters; Measurement uncertainty; Power system dynamics; State estimation; Transmission line measurements; Bad data; iterative extended Kalman filter; normalized residual test; power systems; sensitivity threshold; state estimation; weighted least squares;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Conference at Illinois (PECI), 2015 IEEE

Conference_Location

Champaign, IL

Type

conf

DOI

10.1109/PECI.2015.7064881

Filename

7064881