On detailed synchronous generator modeling for massively parallel dynamic state estimation

Author

Karimipour, Hadis ; DINAVAHI, VENKATA

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, AB, Canada

fYear

2014

fDate

7-9 Sept. 2014

Firstpage

1

Lastpage

6

Abstract

Synchronous generators are normally represented in a simplified fashion to reduce computational complexity in dynamic state estimation (DSE). In this paper a dynamic state estimator for a sixth-order synchronous generator model was developed on the massively parallel graphic processing units (GPU) to provide detailed and accurate Extended Kalman Filter (EKF) based estimation of the generator states. The estimation results are compared with the time domain simulation results on the CPU to demonstrate the accuracy of the proposed method. Also a speed-up of 10.02 for a 5120 generator system is reported.

Keywords

Kalman filters; computational complexity; graphics processing units; nonlinear filters; parallel programming; power system analysis computing; power system state estimation; synchronous generators; EKF; GPU; computational complexity reduction; computational speed; extended Kalman filter based estimation; massively parallel dynamic state estimation; massively parallel graphic processing units; parallel programming; power system dynamic state estimation; sixth-order synchronous generator modeling; Computational modeling; Graphics processing units; Mathematical model; Power system dynamics; State estimation; Synchronous generators; Dynamic state estimation; extended Kalman filter; graphic processing units; large-scale systems; parallel programming; synchronous generator model;

fLanguage

English

Publisher

ieee

Conference_Titel

North American Power Symposium (NAPS), 2014

Conference_Location

Pullman, WA

Type

conf

DOI

10.1109/NAPS.2014.6965417

Filename

6965417