Power System Dynamic State Estimation Based on a New Particle Filter
Autor: | Chen Huanyuan, Yao Cheng, She Caiqi, Liu Xindong |
---|---|
Jazyk: | angličtina |
Předmět: |
Engineering
business.industry mixed measurement Kalman filter Simultaneous localization and mapping Mixed Kalman Particle Filter simulation Invariant extended Kalman filter Computer Science::Robotics Extended Kalman filter Control theory Computer Science::Systems and Control General Earth and Planetary Sciences Ensemble Kalman filter Fast Kalman filter Unscented transform Dynamic state estimation business Alpha beta filter General Environmental Science Power system |
Zdroj: | Procedia Environmental Sciences. :655-661 |
ISSN: | 1878-0296 |
DOI: | 10.1016/j.proenv.2011.12.102 |
Popis: | In order to improve the performance of power system dynamic state estimation, a new particle filter for nonlinear filtering problems (Mixed Kalman Particle Filter, MKPF) is introduced. The MKPF method which based on the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), can obtain a more accurate approximate expression of the true distribution. Combined with the real-time data of mixed measurement (WAMS/SCADA), a simulation of power system dynamic state estimation is established. Finally, the simulation results show that the method can quickly follow to the real value after the power system is disturbed and obtain higher estimated accuracy and robustness than the EKF and UKF methods. |
Databáze: | OpenAIRE |
Externí odkaz: |