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Non-technical Losses Detection: An Innovative No-Neutral Detector Device for Tampered Meters

  • Fabrício P. Santilio
  • Raul V. A. MonteiroEmail author
  • Arnulfo B. de Vasconcellos
  • Nicolás E. Cortez
  • Rodolfo Quadros
  • Antônio de P. Finazzi
Article
  • 36 Downloads

Abstract

The increase in electric energy tariffs in conjunction with social and cultural aspects often leads electric energy consumers to tamper with electric energy meters, as a means to decrease the reading for consumed energy at their respective residencies. One form of meter tampering that is very difficult to detect is the removal of the neutral conductor, which goes on to produce an effect where the meter registers a consumption that is lower than the real consumption. The identification of this fraud is difficult due to the fact that the readings are scheduled, and as such the fraudulent consumer needs only to reconnect the neutral conductor at the time of the meter reading. This article presents a device developed for identifying and registering this type of theft, thus making it more difficult to practice. Laboratory tests and simulations have shown that the lower the power factor of an installation in low voltage, lower will be the reading registered on the meter when this type of fraud arises. This newly developed device has shown itself to be robust, with a low cost as well as efficient in performing to its proposed intent.

Keywords

Non-technical losses Meter tampering Fraud Prototype 

Notes

Acknowledgements

The authors would like to thank the Federal University of Mato Grosso for their support and access to its physical space for the development of this study.

References

  1. ANEEL – Brazilian Electricity Regulatory Agency. (2017). Tariff flags. http://www.aneel.gov.br/tarifas-consumidores/-/asset_publisher/e2INtBH4EC4e/content/bandeira-tarifaria/654800?inheritRedirect=false. Accessed 6 June 2019.
  2. ANEEL – Brazilian Electricity Regulatory Agency. (2018a). Energy loss. http://www.aneel.gov.br/metodologia-distribuicao/-/asset_publisher/e2INtBH4EC4e/content/perdas/654800?inheritRedirect=false. Accessed 6 June 2019.
  3. ANEEL – Brazilian Electricity Regulatory Agency. (2018b). Procedures for electrical energy distribution in the national electric system: Prodist module 8. http://www.aneel.gov.br/documents/656827/14866914/M%C3%B3dulo_8-Revis%C3%A3o_10/2f7cb862-e9d7-3295-729a-b619ac6baab9. Accessed 6 June 2019.
  4. Chen, S., Zhan, T., Huang, C., Chen, J., & Lin, C. (2015). Nontechnical loss and outage detection using error-based fuzzy petri nets in micro-distribution systems. IEEE Transactions on Smart Grid, 6(1), 411–420.CrossRefGoogle Scholar
  5. Costa, B. C., Alberto, B. L. A., Portela, A. M., Maduro, W., & Eler, O. (2013). Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process. International Journal of Artificial Intelligence & Applications, 4(6), 17–23.CrossRefGoogle Scholar
  6. Czechowski, R., & Kosek, A. M. (2016). The most frequent energy theft techniques and hazards in present power energy consumption. In IEEE proceedings of 2016 joint workshop on cyber-physical security and resilience in smart grids (pp. 1–7).Google Scholar
  7. Depuru, S. S. S. R., Wang, L., & Devabhaktuni, V. (2011). Electricity theft: Overview, issues, prevention and a smart meter based approach to control theft. Energy Policy, 39(2), 1007–1015.CrossRefGoogle Scholar
  8. Dos Angelos, E. W. S., Saavedra, O. R., Cortés, O. A. C., & De Souza, A. N. (2011). Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Transactions on Power Delivery, 26(4), 2436–2442.CrossRefGoogle Scholar
  9. Fitzgerald, A. E., Jr., Kingsley, C., & Umans, S. D. (2003). Electric machinery. New York: McGraw-Hill.Google Scholar
  10. Han, W., & Xiao, Y. (2017). A novel detector to detect colluded non-technical loss frauds in smart grid. Computer Networks, 117, 19–31.CrossRefGoogle Scholar
  11. Huang, S., Lo, Y., & Lu, C. (2013). Non-technical loss detection using state estimation and analysis of variance. IEEE Transactions on Power Systems, 28(3), 2959–2966.CrossRefGoogle Scholar
  12. Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., & Mishra, S. (2016). Decision tree and SVM-based data analytics for theft detection in smart grid. EEE Transactions on Industrial Informatics, 12(3), 1005–1016.CrossRefGoogle Scholar
  13. Jovcic, D., & Baulcombe, P. D. (2011). Instantaneous power theory and applications to power conditioning. In CEUR workshop proceedings (Vol. 1542, No. 3, pp. 33–36).Google Scholar
  14. Leite, J. B., & Mantovani, J. R. S. (2018). Detecting and locating non-technical losses in modern distribution networks. IEEE Transactions on Smart Grid, 9(2), 1023–1032.CrossRefGoogle Scholar
  15. Lin, C., Chen, S., Kuo, C., & Chen, J. (2014). Non-cooperative game model applied to an advanced metering infrastructure for non-technical loss screening in micro-distribution systems. IEEE Transactions on Smart Grid, 5(5), 2468–2469.CrossRefGoogle Scholar
  16. Messini, G. M., & Hatziargyriou, N. D. (2018). Review of non-technical loss detection methods. Electric Power Systems Research, 158, 250–266.CrossRefGoogle Scholar
  17. Nagi, J., Yap, K. S., Tiong, S. K., & Ahmed, S. K. (2010). Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Transactions on Power Delivery, 25(2), 1162–1171.CrossRefGoogle Scholar
  18. Neto, E. A. C. A., & Coelho, J. (2013). Probabilistic methodology for technical and non-technical losses estimation in distribution system. Electric Power Systems Research, 97, 93–99.CrossRefGoogle Scholar
  19. Nizar, A. H., Dong, Z. Y., & Wang, Y. (2008). Power utility nontechnical loss analysis with extreme learning machine method. IEEE Transactions on Power Systems, 23(3), 946–955.CrossRefGoogle Scholar
  20. Singh, B., Chandra, A., & Al-Haddad, K. (2015). Power quality: Problems and mitigation techniques. London: Wiley.Google Scholar
  21. Tariq, M., & Poor, H. V. (2016). Electricity theft detection and localization in grid-tied microgrids. IEEE Transactions on Smart Grid, 9(3), 1920–1929.Google Scholar
  22. Trevizan, R. D., & Bretas, A. S. (2009). Nontechnical losses detection : a discrete cosine transform and optimum-path forest based approach. In 2015 North American power symposium (pp. 1–6).Google Scholar
  23. Xiao, Z., Xiao, Y., & Du, D. H. (2013). Exploring malicious meter inspection in neighborhood area smart grids. IEEE Transactions on Smart Grid, 4(1), 214–226.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Federal University of Mato Grosso (UFMT)CuiabáBrazil

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