Title: Single Iteration Conditional Based DSE Considering Spatial and Temporal Correlation,
Abstract: The increasing complexity of distribution network calls for advancement in
distribution system state estimation (DSSE) to monitor the operating conditions
more accurately. Sufficient number of measurements is imperative for a reliable
and accurate state estimation. The limitation on the measurement devices is
generally tackled with using the so-called pseudo measured data. However, the
errors in pseudo data by cur-rent techniques are quite high leading to a poor
DSSE. As customer loads in distribution networks show high cross-correlation in
various locations and over successive time steps, it is plausible that
deploying the spatial-temporal dependencies can improve the pseudo data
accuracy and estimation. Although, the role of spatial dependency in DSSE has
been addressed in the literature, one can hardly find an efficient DSSE
framework capable of incorporating temporal dependencies present in customer
loads. Consequently, to obtain a more efficient and accurate state estimation,
we propose a new non-iterative DSSE framework to involve spatial-temporal
dependencies together. The spatial-temporal dependencies are modeled by
conditional multivariate complex Gaussian distributions and are studied for
both static and real-time state estimations, where information at preceding
time steps are employed to increase the accuracy of DSSE. The efficiency of the
proposed approach is verified based on quality and accuracy indices, standard
deviation and computational time. Two balanced medium voltage (MV) and one
unbalanced low voltage (LV) distribution case studies are used for evaluations.