Innovative methodology to identify errors in electric energy measurement systems in power utilities

Marco Toledo-Orozco, Carlos Arias-Marin, Carlos Álvarez-Bel, Diego Morales-Jadan, Javier Rodríguez-García, Eddy Bravo-Padilla

Research output: Contribution to journalArticlepeer-review

Abstract

Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.

Original languageEnglish
Article number958
JournalEnergies
Volume14
Issue number4
DOIs
StatePublished - 2 Feb 2021

Bibliographical note

Funding Information:
This research received funding from the project Smart GrI+D+i from the Universidad Cat?lica de Cuenca in Ecuador.

Funding Information:
Funding: This research received funding from the project Smart GrI+D+i from the Universidad Católica de Cuenca in Ecuador.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Artificial intelligence
  • Consumption patterns
  • Data analytics
  • Electrical energy losses
  • Machine learning
  • Outlier detection

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