# Distributed Gauss-Newton Method for State Estimation Using Belief Propagation

arXiv: https://arxiv.org/abs/1702.05781 · HTML: https://mlsanity.com/paper/1702.05781/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [State Estimation in Electric Power Systems Leveraging Graph Neural Networks](https://mlsanity.com/paper/2201.04056/) (2022)
- [Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks](https://mlsanity.com/paper/2207.11465/) (2022)
- [Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks](https://mlsanity.com/paper/2206.02731/) (2022)
- [Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation](https://mlsanity.com/paper/2303.00105/) (2023)

## Abstract (arXiv)

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 main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss-Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation.