Prevention and Detection of Electricity Theft of Distribution Network

Sajad Ali, Min Yongzhi () and Wajid Ali
Additional contact information
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

Sustainability, 2023, vol. 15, issue 6, 1-19

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.

Keywords: smart meter; electricity theft; neural networks; arduino; line losses (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/6/4868/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/6/4868/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:6:p:4868-:d:1092176

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().