A Particle Filter for Dynamic State Estimation in Multi-Machine Systems With Detailed Models

Particle filters provide a general framework for dynamic state estimation (DSE) in nonlinear systems. The scope for particle filter-based DSE can be significantly enhanced by exploiting data from phasor measurement units (PMUs) when available at higher sampling frequencies. In this paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The filter relies on typical output measurements assumed available from PMUs stationed at generator buses. The performance of the proposed filter is illustrated with dynamic simulations on IEEE 14-bus system including: 1) generators models with subtransient dynamics, 2) excitation units (IEEE DC1A, DC2A, AC5A), and 3) turbine-governor models (steam and hydro). The estimation accuracy of the proposed filter is assessed for three classes of disturbances assuming noisy PMUs' measurements and comparative results are presented with the unscented Kalman filter (UKF). The accuracy-computational burden trade-off is also analyzed and the results strengthen the feasibility of using particle filters for dynamic state estimation.