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Distributed state estimation for large-scale nonlinear systems: A reduced order particle filter implementation

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2 Author(s)
Arash Mohammadi ; Computer Science and Engineering, York University, Toronto, ON, Canada ; Amir Asif

Motivated by state estimation problems in power distribution networks (PDN), the paper proposes a fusion based, reduced order, distributed implementation of the particle filter (FR/DPF) for large scale, nonlinear dynamical systems with localized sensor observations. Direct application of the centralized particle filter is computationally challenging due to the high dimensions of the state-space dynamics. Based on partitioning the overall system into N localized but mathematically coupled subsystems, the near-optimal FR/DPF provides computational savings of a factor of N over the centralized particle filter implementation. By introducing distributed state and observation fusion steps, the proposed FR/DPF does not require a fusion centre and maintains consistency between the local sub-systems. In our Monte Carlo simulations of a simplified PDN, the performance of the FR/DPF is consistently close to that of the centralized implementation.

Published in:

2012 IEEE Statistical Signal Processing Workshop (SSP)

Date of Conference:

5-8 Aug. 2012