Spatial-temporal state estimation using CMCGD applied to distribution networks

The safe and satisfactory operation of emerging distribution networks with distributed and renewable resources would normally require large number of smart meters and sensing devices that introduce significant costs. An alternative solution can be introduced by effective use of pseudo measurements along with enhanced state estimation methods. This paper proposes temporal and spatial correlation among loads as part of the modelling for an efficient state estimator, which is based on Conditional Multivariate Complex Gaussian Distribution (CMCGD) method. This approach can significantly reduce the error, measured by the Standard Deviation (SD) of the pseudo measurements. Finally, the real and pseudo measurements are included in this technique to estimate accurate branch currents and bus voltages. This method is applied to a distribution network and the results are presented to show the effectiveness of the proposed non-iterative algorithm for distribution networks with low numbers of measurement points.