CUDA-Based Parallel Computation Model for State Estimation

With the development of the active distribution network (ADN), distributed state estimation (DSE) has become an inevitable trend for state estimation (SE). The efficient partitioned strategy is an essential prerequisite for DSE. However, the existing methods only consider the basic requirements of equilibrium connectivity, and the similarity of buses in one sub-region is ignored. A new partitioned strategy is proposed in this paper, this method learns from the idea of hierarchical clustering, the buses of the distribution network are aggregated to form sub-regions, and CUDA platform is used to realize the parallel computation of SE. The improved IEEE33-node system and a real distribution network are analyzed as a case study. The results show that, compared with traditional CSE, the estimation accuracy of DSE is higher than that of CSE, which indicates that the estimation accuracy of the proposed method is higher than that of CSE. Besides, compared with serial computation, this method can effectively reduce the running time of DSE in each sub-region and improve the overall calculation efficiency.

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