  This paper presents new dynamic topology adaptation strategies for
distributed estimation in smart grids systems. We propose a dynamic exhaustive
search--based topology adaptation algorithm and a dynamic sparsity--inspired
topology adaptation algorithm, which can exploit the topology of smart grids
with poor--quality links and obtain performance gains. We incorporate an
optimized combining rule, named Hastings rule into our proposed dynamic
topology adaptation algorithms. Compared with the existing works in the
literature on distributed estimation, the proposed algorithms have a better
convergence rate and significantly improve the system performance. The
performance of the proposed algorithms is compared with that of existing
algorithms in the IEEE 14--bus system.
