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

TL;DR

This work tackles topological path identification (TPI) in power distribution networks under incomplete data by proposing a systematic procedure that converts heterogeneous raw information into well-defined data via transformation functions, constructs a comprehensive hypothetical path set, and iteratively narrows to paths compatible with the available information using a diagnostic validation. The method accommodates both active and backup paths and is designed to adapt to diverse network configurations, enhancing accuracy and facilitating digital twin development. A scalability discussion introduces an expanding path set (EPS) technique to mitigate combinatorial blow-up, and an academic example demonstrates practical applicability and iterative refinement with data augmentation. The approach thus bridges data availability and network-expertise to yield robust TPI in real-world DSOs.

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.

Figures (7)

• Figure 1: Real network, Fig. \ref{['fig:realrec']}a, and the approximate network when considering only the elements known by the DSO, Fig. \ref{['fig:realrec']}b. It is possible to note some inaccuracies in the GPS location and some missing elements.
• Figure 2: Transformation functions transforming different kinds of data into well-defined information.
• Figure 3: Application of the systematic procedure to an academic power distribution network. Some hypothetical paths are shown in Fig. \ref{['fig:example']}a. Figure \ref{['fig:example']}b shows the network situation after excluding the paths not compatible with the well-defined information from the hypothetical path set. Figure \ref{['fig:example']}c shows the network situation after modifying the transformation functions, $\mathcal{F}$, to take into account some identified issues. After finding a solution to the TPI problem, it is possible to identify the active and backup paths, as illustrated in Fig. \ref{['fig:example']}c.
• Figure 4: Active path for customer $\hat{e}_{10}$
• Figure 5: Active path for customer $\hat{e}_{11}$