We present a novel distributed Gauss-Newton method for the non-linear state estimation
(SE) model based on a probabilistic inference method called belief propagation (BP). The …

As the integration of High Voltage Direct Current (HVDC) systems on modern power
networks continues to expand, challenges have appeared in different fields of the network …

The development of state estimators for local electrical energy supply systems is inevitable
as the role of the system's become more important, especially with the recent increased …

The state estimation algorithm estimates the values of the state variables based on the
measurement model described as the system of equations. Prior to applying the state …

We consider the problem of maximum-likelihood estimation in linear models represented by
factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by …

Considering the increase in power system size and the number of PMUs, the utilization of a
computationally efficient yet accurate static state estimator is crucial. The fast-decoupled …

To maintain stable grid operations, system monitoring and control processes require the
computation of grid states (eg, voltage magnitude and angles) at high granularity. It is …

Smart grids promise a more reliable, efficient, economically viable, and environment-friendly
electricity infrastructure for the future. State estimation in smart grids plays a pivotal role in …

This paper proposes a solution to the state estimation problem in gas networks using the
distributed belief propagation (BP) algorithm. Power system identification applications …

Estimating the system state is a non-trivial task given a large set of measurements, fuelling
the ongoing research attempts to find efficient, scalable and fast state estimation (SE) …