Topology and Admittance Estimation: Precision Limits and Algorithms

TL;DR

This paper investigates the fundamental limits of estimating distribution grid topology and admittance from noisy measurements, and introduces a self-adaptive algorithm that approaches these limits in practical scenarios.

Contribution

It provides a theoretical analysis of the precision limits for topology and admittance estimation and proposes a novel adaptive algorithm that performs near these limits.

Findings

CPS algorithm approaches theoretical precision limits

Effective under various measurement noise levels

Validated on IEEE 33 and 141-bus systems

Abstract

Distribution grid topology and admittance information are essential for system planning, operation, and protection. In many distribution grids, missing or inaccurate topology and admittance data call for efficient estimation methods. However, measurement data may be insufficient or contaminated with large noise, which will introduce fundamental limits to the estimation accuracy. This work explores the theoretical precision limits of the topology and admittance estimation (TAE) problem, with different measurement devices, noise levels, and the number of measurements. On this basis, we propose a conservative progressive self-adaptive (CPS) algorithm to estimate the topology and admittance. Results on IEEE 33 and 141-bus systems validate that the proposed CPS method can approach the theoretical precision limits under various measurement settings.