A Systematic Procedure for Topological Path Identification with Raw Data Transformation in Electrical Distribution Networks

TL;DR

This paper presents a systematic, iterative method for topological path identification in power distribution networks, transforming raw data into exploitable information to improve accuracy and adaptability for DSOs.

Contribution

The paper introduces a novel iterative procedure that transforms raw data and refines path identification, enhancing accuracy and flexibility in distribution network topology detection.

Findings

Improved accuracy in path identification.

High adaptability to diverse network configurations.

Effective handling of incomplete or inaccurate data.

Abstract

This paper introduces a systematic approach to address the topological path identification (TPI) problem in power distribution networks. Our approach starts by listing the DSO's raw information coming from several sources. The raw information undergoes a transformation process using a set of transformation functions. This process converts the raw information into well-defined information exploitable by an algorithm. Then a set of hypothetical paths is generated, considering any potential connections between the elements of the power distribution system. This set of hypothetical paths is processed by the algorithm that identifies the hypothetical paths that are compatible with the well-defined information. This procedure operates iteratively, adapting the set of transformation functions based on the result obtained: if the identified paths fail to meet the DSO's expectations, new data is collected, and/or the transformation functions found to be responsible for the discrepancies are modified. The systematic procedure offers practical advantages for DSOs, including improved accuracy in path identification and high adaptability to diverse network configurations, even with incomplete or inaccurate data. Consequently, it emerges as a useful tool for the construction of digital twins of power distribution networks that aligns with DSO expectations.