Smart Grids data characterization: a revision

• Autores: Leonardo Minelli, Jonas Fernando Schreiber, Paulo Sergio Sausen, Airam Teresa Zago Romcy Sausen, Maurício de Campos
• Localización: Cuadernos de Educación y Desarrollo, ISSN-e 1989-4155, Vol. 16, Nº. 2, 2024
• Idioma: inglés
• DOI: 10.55905/cuadv16n2-042
• Enlaces
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• Resumen

  • This research explores the characterization of data in time series in Smart Grids, considering the importance of data as a basis for information and knowledge. The analysis, based on real data from a Smart Grid, focused on quantities such as temperature, voltage and current. Characteristics such as stationarity, linearity, complexity, cyclicality, mutability and randomness were addressed. The application of these characteristics made it possible to identify specific patterns and behaviors in each piece of data. Stationarity, linearity, and randomness are properties that can vary over time, and it is crucial to analyze time series at different periods. In addition, additional Big Data characteristics, such as trueness, value, variability, and others, amplify the complexity of the analysis. The research provides relevant insights to understand and address the challenges in analyzing large volumes of smart power grid data.

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