Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter
作者： Ning Zhou , Da Meng , Shuai Lu
DOI: 10.1109/TPWRS.2013.2262236
关键词: Kalman filter 、 Unscented transform 、 Control theory 、 Engineering 、 Ensemble Kalman filter 、 Phasor measurement unit 、 Monte Carlo method 、 Invariant extended Kalman filter 、 Extended Kalman filter 、 Particle filter
摘要: In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates mean and covariance via Monte Carlo simulation, easy implement, can be directly applied nonlinear system with non-Gaussian noise. The improves robustness basic through iterative sampling inflation dispersion. Using simulations practical noise model uncertainty considerations, PF's performance evaluated compared PF, Kalman (EKF) unscented (UKF). results showed high accuracy against
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