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

fYear

2011

fDate

4-6 Aug. 2011

Firstpage

1

Lastpage

5

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NAPS.2011.6024897

Filename

6024897