# Dynamic Distribution State Estimation Using Synchrophasor Data

## TL;DR

This paper presents a fast, robust, and real-time algorithm for distribution state estimation using streaming synchrophasor data, leveraging time-varying optimization and prediction-correction methods.

## Contribution

It introduces a novel online algorithm for dynamic distribution state estimation that is computationally efficient and capable of tracking states under measurement outliers.

## Key findings

- Algorithm provably tracks state variables accurately.
- Computational efficiency achieved without matrix-inverse calculations.
- Robustness to measurement outliers demonstrated.

## Abstract

The increasing deployment of distribution-level phasor measurement units (PMUs) calls for dynamic distribution state estimation (DDSE) approaches that tap into high-rate measurements to maintain a comprehensive view of the distribution-system state in real time. Accordingly, this paper explores the development of a fast algorithmic framework by casting the DDSE task within the time-varying optimization realm. The time-varying formulation involves a time-varying robustified least-squares approach, and it naturally models optimal trajectories for the estimated states under streaming of measurements. The formulation is based on a linear surrogate of the AC power-flow equations, and it includes an element of robustness with respect to measurement outliers. The paper then leverages a first-order prediction-correction method to achieve simple online updates that can provably track the state variables from heterogeneous measurements. This online algorithm is computationally efficient as it relies on the Hessian of the cost function without computing matrix-inverse. Convergence and bounds on the estimation errors of proposed algorithm can be analytically established.