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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.
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Date of Conference: 5-8 Aug. 2012