Anti-Electricity Theft Model for Sustainable Economic Development in Nigeria
Abstract
In Nigeria today, as in most parts of the world, Power Distribution Companies face several challenges, among which is electricity theft. Electricity theft is simply the practice of bypassing, stealing, or utilizing electricity without payment. The most common forms of electricity theft in Nigeria, for instance, are meter bypass and illegal hook connections by consumers who attempt to defraud the system by not paying for power used in their households and businesses. A schematic method is adopted to mitigate this pervasive issue, ensuring that authorities have real-time knowledge when consumers engage in illicit practices. The designed system hybridizes technical and non-technical methods, integrating a smart resistance module capable of controlling both technical and non-technical losses caused by consumers, thereby enhancing the scheme's sensitivity. This system incorporates a feedback mechanism designed for easy communication to the control room, which will inject third harmonics into the consumer’s line immediately upon detection of meter bypass. Simulation was conducted using Simulink MATLAB, and the results obtained indicated that the system offers significant improvement compared to approaches that tackle either non-technical or technical aspects independently.
References
N. O. Shokoya and A. K. Raji, "Electricity theft mitigation in the Nigerian power sector," International Journal of Engineering & Technology, vol. 8, no. 4, pp. 467–472, 2019. doi: 10.14419/ijet.v8i4.29391.
A. A. Adedeji, "Spatial exploration and analysis of electricity poverty: a case study of Ibadan, Southwestern, Nigeria," Ph.D. dissertation, Univ. of Leicester, Leicester, U.K., 2016. [Online]. Available: https://hdl.handle.net/2381/37461
K. Jeremiah, "Energy theft registry will reduce electricity challenges in Nigeria," The Guardian, 2024. [Online]. Available: https://guardian.ng/energy/energy-theft-registry-will-reduce-electricity-challenges-in-nigeria/. [Accessed: Jun. 2024].
L. Viegas, P. R. Esteves, R. Melício, V. M. F. Mendes, and S. M. Vieira, "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, vol. 80, pp. 1256–1268, 2017. doi: 10.1016/j.rser.2017.05.193.
World Bank, Non-technical losses in electricity distribution systems. [Online]. Available: https://www.worldbank.org. [Accessed: May 2024].
Energy Sector Management Assistance Program (ESMAP), Electricity distribution and non-technical losses in India. [Online]. Available: https://www.esmap.org. [Accessed: May 2024].
Nigerian Electricity Regulatory Commission (NERC), Electricity theft: A threat to Nigeria’s energy security. [Online]. Available: https://www.nerc.gov.ng. [Accessed: June 2024].
Accenture, Using machine learning to reduce electricity theft in Brazil. [Online]. Available: https://www.accenture.com. [Accessed: Jul. 2024].
J. B. Leite and J. R. S. Mantovani, "Detecting and locating non-technical losses in modern distribution networks," IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1023–1032, 2018. doi: 10.1109/TSG.2016.2574714.
D. Terungwa, "Federal Government pledges to combat electricity theft and safeguard power assets from vandals," 2024. [Online]. Available: https://www.majorwavesenergyreport.com/federal-government-pledges-to-combat-electricity-theft/. [Accessed: Apr. 2024].
E. Addeh, "NERC: Over 25% energy wheeled by TCN lost to theft, technical inefficiency," This Day, 2021. [Online]. Available: https://www.thisdaylive.com/index.php/2021/12/21/nerc-over-25-energy-wheeled-by-tcn-lost-to-theft-technical-inefficiency/. [Accessed: Jun. 2024].
G. M. Shafiullah, A. T. Chan, and A. Bakar, "Detection of electricity theft using smart metering data and machine learning," IEEE Transactions on Smart Grid, vol. 14, no. 1, pp. 273–285, Jan. 2023, doi: 10.1109/TSG.2022.3176882.
N. Zhang, Z. Yang, and J. Hou, "Power quality monitoring and theft detection with harmonic distortion analysis," IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4578–4589, May 2022, doi: 10.1109/TIE.2021.3104351.
H. Bevrani, M. Watanabe, and Y. Mitani, "Power system monitoring and control using virtual synchronous generators," IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2854–2864, July 2022, doi: 10.1109/TSG.2022.3147953.
A. O. Adeoye, A. A. Jimoh, and T. T. Matidza, "Electricity theft detection using current harmonics and S-transform," IEEE Access, vol. 10, pp. 110395–110405, 2022, doi: 10.1109/ACCESS.2022.3212536.
S. U. Khan, Z. Ullah, and F. Aadil, "Hybrid machine learning approach for electricity theft detection in smart grids," IEEE Access, vol. 9, pp. 113478–113487, 2021, doi: 10.1109/ACCESS.2021.3104356.
Copyright (c) 2025 Josephine Sandra Ndulaka, Emmanuel Nwele, Matthew Olubiwe, Isdore Onyema Akwukwaegbu

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.















