Title: Layered Based Augmented Complex Kalman Filter for Fast Forecasting-Aided State Estimation of Distribution Networks,
Abstract: In the presence of renewable resources, distribution networks have become
extremely complex to monitor, operate and control. Furthermore, for the real
time applications, active distribution networks require fast real time
distribution state estimation (DSE). Forecasting aided state estimator (FASE),
deploys measured data in consecutive time samples to refine the state estimate.
Although most of the DSE algorithms deal with real and imaginary parts of
distribution networks states independently, we propose a non iterative complex
DSE algorithm based on augmented complex Kalman filter (ACKF) which considers
the states as complex values. In case of real time DSE and in presence of a
large number of customer loads in the system, employing DSEs in one single
estimation layer is not computationally efficient. Consequently, our proposed
method performs in several estimation layers hierarchically as a Multi layer
DSE using ACKF (DSEMACKF). In the proposed method, a distribution network can
be divided into one main area and several subareas. The aggregated loads in
each subarea act like a big customer load in the main area. Load aggregation
results in a lower variability and higher cross correlation. This increases the
accuracy of the estimated states. Additionally, the proposed method is
formulated to include unbalanced loads in low voltage (LV) distribution
network.