OntoFreya: A Power Distribution Ontology for Electric Metrics Classification

Power utilities demand large volumes of data used in power distribution networks. Among them are parameters representing possible technical failures, such as network’s short circuit current and voltage sag. Specialists find these parameters and detect technical failures. However, this process can become time-consuming. Thus, this article proposes an ontology called OntoFreya, which classifies voltage, current, or any electric metric, following the definitions of the regulatory agencies and reducing the time spent on this task. A series of 4402 axioms, 132 classes, and 40 data properties comprises OntoFreya. The ontology automatically inferred classifications for four hundred readings from energy samples, validating OntoFreya across three scenarios. The first and second scenarios classified current in amperes, and the third classified voltage in per-unit system (pu). The scenarios showed that OntoFreya automates the classification of electric metrics, reducing specialists’ time in detecting technical failures in a distribution network.

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Publication Details

Journal
JUCS - Journal of Universal Computer Science
Published
2026-03-23
DOI
https://doi.org/10.3897/jucs.145075
Primary Topic
Electricity Theft Detection Techniques
Type
article
Field-Weighted Citation Impact
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article

OntoFreya: A Power Distribution Ontology for Electric Metrics Classification

Jorge Aranda, Jorge Luís Victória Barbosa, Eleandro Luis Marques da Silva, Paulo Ricardo da Silva Pereira et al.
JUCS - Journal of Universal Computer Science
Electricity Theft Detection Techniques
article

OntoFreya: A Power Distribution Ontology for Electric Metrics Classification

Jorge Aranda, Jorge Luís Victória Barbosa, Eleandro Luis Marques da Silva, Paulo Ricardo da Silva Pereira, Marcelo Pinto Vianna, Vitor Werner de Vargas, Ricardo dos Santos Costa
article en

Abstract

Power utilities demand large volumes of data used in power distribution networks. Among them are parameters representing possible technical failures, such as network’s short circuit current and voltage sag. Specialists find these parameters and detect technical failures. However, this process can become time-consuming. Thus, this article proposes an ontology called OntoFreya, which classifies voltage, current, or any electric metric, following the definitions of the regulatory agencies and reducing the time spent on this task. A series of 4402 axioms, 132 classes, and 40 data properties comprises OntoFreya. The ontology automatically inferred classifications for four hundred readings from energy samples, validating OntoFreya across three scenarios. The first and second scenarios classified current in amperes, and the third classified voltage in per-unit system (pu). The scenarios showed that OntoFreya automates the classification of electric metrics, reducing specialists’ time in detecting technical failures in a distribution network.

JUCS - Journal of Universal Computer ScienceVol. 32(3)
Federal Institute of São Paulo (BR), Agência Nacional de Energia Elétrica (BR), Reference Center Foundation for Innovative Technologies (BR), Universidade do Vale do Rio dos Sinos (BR)
Affordable and clean energy
Openalex Percentile: Top 12%
Electricity Theft Detection Techniques
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