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Title: Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter

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 the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a non-linear system with non-Gaussian noise. The extended PF modifies a basic PF to improve robustness. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF’s performance is evaluated and compared with the basic PF and an extended Kalman filter (EKF). The extended PF results showed high accuracy and robustness against measurement and model noise.
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Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Power Systems, 28(4):4152-4161
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
Country of Publication:
United States
particle filter, phasor measurement unit (PMU), state estimation, power system dynamics, extended Kalman filter (EKF).