PROJECT TITLE :

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

ABSTRACT:

Particle filters give a general framework for dynamic state estimation (DSE) in nonlinear systems. The scope for particle filter-based DSE will be considerably enhanced by exploiting information from phasor measurement units (PMUs) when on the market at higher sampling frequencies. During this paper, we have a tendency to gift a particle filtering approach to dynamically estimate the states of a synchronous generator during a multi-machine setting considering the excitation and prime mover control systems. The filter relies on typical output measurements assumed out there from PMUs stationed at generator buses. The performance of the proposed filter is illustrated with dynamic simulations on IEEE 14-bus system together with: one) 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 3 categories 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.