Title :
A regression analysis based state transition model for power system dynamic state estimation
Author :
Hassanzadeh, Mehrdad ; Evrenosoglu, Cansin Yaman
Author_Institution :
Electr. & Biomed. Eng. Dept., Univ. of Nevada, Reno, NV, USA
Abstract :
In this paper, a new regression analysis based method is proposed to calculate the power system state transition matrix. This matrix is used to predict the system state which is subsequently corrected through extended Kalman filter in classical dynamic state estimation (DSE). State transition matrix is calculated by using regression analysis for a specified time interval and updated once new online measurement data are available. The preliminary tests on IEEE 14-bus system show improvement in the state forecasting accuracy when compared to existing state forecasting methods in dynamic state estimation.
Keywords :
matrix algebra; power system state estimation; regression analysis; IEEE 14-bus system; extended Kalman filter; online measurement data; power system dynamic state estimation; power system state transition matrix; regression analysis; state forecasting accuracy; state forecasting methods; state transition model; time interval; Accuracy; Equations; Forecasting; Mathematical model; Power system dynamics; State estimation;
Conference_Titel :
North American Power Symposium (NAPS), 2011
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-0417-8
Electronic_ISBN :
978-1-4577-0418-5
DOI :
10.1109/NAPS.2011.6024897
