Title :
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
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;
Conference_Titel :
Power and Energy Conference at Illinois (PECI), 2015 IEEE
Conference_Location :
Champaign, IL
DOI :
10.1109/PECI.2015.7064881
