Distributed dynamic state estimation with extended Kalman filter

Author

Pengwei Du ; Zhenyu Huang ; Yannan Sun ; Ruisheng Diao ; Kalsi, Karanjit ; Anderson, K.K. ; Yulan Li ; Lee, Bang-Wook

Author_Institution

Pacific Northwest Nat. Lab., Richland, WA, USA

fYear

2011

fDate

4-6 Aug. 2011

Firstpage

1

Lastpage

6

Abstract

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman Alter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

Keywords

Kalman filters; computational complexity; power system control; power system measurement; power system state estimation; renewable energy sources; smart power grids; 68-bus test system; PMU; computation complexity; decentralized computing resources; distributed dynamic state estimation; domain decomposition method; extended Kalman filter; large-scale renewable resources; phasor measurement data; smart-grid technologies; Computational modeling; Generators; Phasor measurement units; Power measurement; Power system dynamics; State estimation; Voltage measurement; Dynamic Simulation; Extended Kalman Filter; Model Sensitivity; Multi-machine Power System;

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.2011.6024863

Filename

6024863