Nonlinear, reduced order, distributed state estimation in microgrids

Recent developments in microgrids place strict constraints on the underlying state estimation technology, including the need for a dynamic and distributed approach. Since the problem is reminiscent of classical information fusion [2], the paper explores the application of a fusion-based reduced order, distributed unscented particle filter (FR/DUPF) for dynamic state estimation in microgrids. By partitioning the nonlinear microgrid into a network of n
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localized and dynamically coupled systems, the FR/DUPF provides computational savings of a factor of n
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over its centralized version. Monte Carlo simulations verify its accuracy by confirming that estimates from the FR/DUPF and centralized filter evolve close to the ground truth.