In this paper principles of extended Kalman filtering theory is developed and applied to simulated on-line electric power systems state estimation in order to trace the operating condition changes through the redundant and noisy measurements. Test results on IEEE 14 - bus test system are included. Three case systems are tried; through the comparing of their results, it is concluded that the proposed dynamic estimator have great advantage over the static state estimation in its accuracy and real time implementation.
Mahdavi Tabatabayee, N., & Kenarangui, R. (1995). On Line Electric Power Systems State Estimation Using Kalman Filtering (RESEARCH NOTE). International Journal of Engineering, 8(4), 233-235.
MLA
N. Mahdavi Tabatabayee; R. Kenarangui. "On Line Electric Power Systems State Estimation Using Kalman Filtering (RESEARCH NOTE)". International Journal of Engineering, 8, 4, 1995, 233-235.
HARVARD
Mahdavi Tabatabayee, N., Kenarangui, R. (1995). 'On Line Electric Power Systems State Estimation Using Kalman Filtering (RESEARCH NOTE)', International Journal of Engineering, 8(4), pp. 233-235.
VANCOUVER
Mahdavi Tabatabayee, N., Kenarangui, R. On Line Electric Power Systems State Estimation Using Kalman Filtering (RESEARCH NOTE). International Journal of Engineering, 1995; 8(4): 233-235.
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