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Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities

Author

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  • Marco Toledo-Orozco

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain
    School of Electrical Engineering, Faculty of Engineering, Universidad de Cuenca, Cuenca 010109, Ecuador)

  • Carlos Arias-Marin

    (Electrical Engineering Career, Universidad Politécnica Salesiana, Sede Cuenca 010103, Ecuador)

  • Carlos Álvarez-Bel

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

  • Diego Morales-Jadan

    (Electrical Engineering Career, Circular Economy Laboratory-CIITT, Universidad Católica de Cuenca, Sede Cuenca 010107, Ecuador)

  • Javier Rodríguez-García

    (Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

  • Eddy Bravo-Padilla

    (School of Electrical Engineering, Faculty of Engineering, Universidad de Cuenca, Cuenca 010109, Ecuador)

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.

Suggested Citation

  • Marco Toledo-Orozco & Carlos Arias-Marin & Carlos Álvarez-Bel & Diego Morales-Jadan & Javier Rodríguez-García & Eddy Bravo-Padilla, 2021. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities," Energies, MDPI, vol. 14(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:958-:d:497895
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    References listed on IDEAS

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    1. Yakubu, Osman & Babu C., Narendra & Adjei, Osei, 2018. "Electricity theft: Analysis of the underlying contributory factors in Ghana," Energy Policy, Elsevier, vol. 123(C), pages 611-618.
    2. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    3. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    4. Anna I. Pózna & Attila Fodor & Katalin M. Hangos, 2019. "Model-based fault detection and isolation of non-technical losses in electrical networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 25(4), pages 397-428, July.
    5. Viegas, Joaquim L. & Esteves, Paulo R. & Melício, R. & Mendes, V.M.F. & Vieira, Susana M., 2017. "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1256-1268.
    6. Daniel Leite & José Pessanha & Paulo Simões & Rodrigo Calili & Reinaldo Souza, 2020. "A Stochastic Frontier Model for Definition of Non-Technical Loss Targets," Energies, MDPI, vol. 13(12), pages 1-20, June.
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