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The increasing complexity of distribution network calls for advancement indistribution system state estimation (DSSE) to monitor the operating conditionsmore accurately. Sufficient number of measurements is imperative for a reliableand accurate state estimation. The limitation on the measurement devices isgenerally tackled with using the so-called pseudo measured data. However, theerrors in pseudo data by cur-rent techniques are quite high leading to a poorDSSE. As customer loads in distribution networks show high cross-correlation invarious locations and over successive time steps, it is plausible thatdeploying the spatial-temporal dependencies can improve the pseudo dataaccuracy and estimation. Although, the role of spatial dependency in DSSE hasbeen addressed in the literature, one can hardly find an efficient DSSEframework capable of incorporating temporal dependencies present in customerloads. Consequently, to obtain a more efficient and accurate state estimation,we propose a new non-iterative DSSE framework to involve spatial-temporaldependencies together. The spatial-temporal dependencies are modeled byconditional multivariate complex Gaussian distributions and are studied forboth static and real-time state estimations, where information at precedingtime steps are employed to increase the accuracy of DSSE. The efficiency of theproposed approach is verified based on quality and accuracy indices, standarddeviation and computational time. Two balanced medium voltage (MV) and oneunbalanced low voltage (LV) distribution case studies are used for evaluations.

Single Iteration Conditional Based DSE Considering Spatial and Temporal Correlation