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Prevention and Detection of Electricity Theft of Distribution Network

Author

Listed:
  • Sajad Ali

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Min Yongzhi

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Wajid Ali

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

Electricity theft is a costly problem. This paper will be focused on Pakistan and the problem of electricity theft. We will discuss its impacts and how best to fix them through the use of technology. For this purpose, we developed a smart meter, focusing on grid modernization through economic smart meter development. This paper focuses on a study carried out with the help of PESCO. It is one of the most inefficient distribution providers. The study has evaluated commercial, industrial, rural, and urban areas, covering a total area of 15 km 2 . The area includes several power sinks. Previous research has been used to compare the results of this case study; this included studies of other Third World countries, such as Pakistan and South Africa. The design of, clever, innovative, intelligent meters used in this study was better than the basic digital meters and had many features compatible with the E.U., and U.S.A.’s western power market and energy infrastructure. The study also discusses the potential use of neural network-trained models and IoT (internet of things) integration with cloud computing. This can provide an alternate means of data analysis, accurate prediction, and greater user accessibility. The case study is the first ever done using smart meters on such a large scale, and the compiled data has provided insight into energy consumers and their usage. The statistics can be used to isolate the most probable cause of theft and the area or location of occurrence.

Suggested Citation

  • Sajad Ali & Min Yongzhi & Wajid Ali, 2023. "Prevention and Detection of Electricity Theft of Distribution Network," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4868-:d:1092176
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    References listed on IDEAS

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    1. Anna Cretì & Fulvio Fontini, 2019. "Economics of Electricity. Markets, Competition and Rules," Post-Print hal-02304345, HAL.
    2. Adongo, Charles Atanga & Taale, Francis & Bukari, Shaibu & Suleman, Shafic & Amadu, Iddrisu, 2021. "Electricity theft whistleblowing feasibility in commercial accommodation facilities," Energy Policy, Elsevier, vol. 155(C).
    3. Faisal Mohammad & Young-Chon Kim, 2020. "Energy load forecasting model based on deep neural networks for smart grids," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 824-834, August.
    4. Faisal Jamil & Eatzaz Ahmad, 2014. "An Empirical Study of Electricity Theft from Electricity Distribution Companies in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(3), pages 239-254.
    5. Jamil, Faisal & Ahmad, Eatzaz, 2019. "Policy considerations for limiting electricity theft in the developing countries," Energy Policy, Elsevier, vol. 129(C), pages 452-458.
    6. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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    Cited by:

    1. Yiran Wang & Shuowei Jin & Ming Cheng, 2023. "A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection," Sustainability, MDPI, vol. 15(13), pages 1-22, June.

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