Data-driven Topology and Parameter Identification in Distribution Systems with limited Measurements

TL;DR

This paper introduces data-driven techniques for identifying switch states, phases, and equipment parameters in low voltage distribution grids with limited measurements, addressing challenges of observability and modeling errors.

Contribution

It presents novel methods tailored for systems with few measurements, employing problem decomposition, heuristics, correlation analysis, and optimization for accurate system identification.

Findings

Methods are validated on a realistic grid.

Techniques show resilience to data quality issues.

Limitations of the methods are discussed.

Abstract

This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors. The proposed methods are tailored for systems with a limited number of spatially distributed measuring devices, which measure voltage magnitudes at specific nodes and some line current magnitudes. The overall approach employs a problem decomposition strategy to divide the problem into smaller subproblems, which are addressed independently. The techniques for identifying switch states and system phases are based on heuristics and a binary optimization problem using correlation analysis of the measured time series. The estimation of equipment parameters is achieved through a data-driven regression approach and by an optimization problem, and the identification of cable types is solved using a Mixed-Integer Quadratic Programming solver. To validate the presented methods, a realistic grid is used and the presented techniques are evaluated for their resilience to data quality and time resolution, discussing the limitations of the proposed methods.