# Distributed State Estimation for AC Power Systems using Gauss-Newton ALADIN

## TL;DR

This paper introduces a distributed state estimation algorithm for AC power systems using a tailored ALADIN method with Gauss-Newton approximations, improving efficiency in handling non-convex power flow equations.

## Contribution

It develops a novel distributed state estimator based on ALADIN with Gauss-Newton Hessian approximations for efficient nonlinear power system estimation.

## Key findings

- Successfully applied to IEEE 30-Bus system
- Demonstrates improved computational efficiency
- Outperforms existing distributed estimation methods

## Abstract

This paper proposes a structure exploiting algorithm for solving non-convex power system state estimation problems in distributed fashion. Because the power flow equations in large electrical grid networks are non-convex equality constraints, we develop a tailored state estimator based on Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method, which can handle the nonlinearities efficiently. Here, our focus is on using Gauss-Newton Hessian approximations within ALADIN in order to arrive at at an efficient (computationally and communicationally) variant of ALADIN for network maximum likelihood estimation problems. Analyzing the IEEE 30-Bus system we illustrate how the proposed algorithm can be used to solve highly non-trivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.