Prevention and Detection of Electricity Theft of Distribution Network

被引:11
作者
Ali, Sajad [1 ]
Min, Yongzhi [1 ]
Ali, Wajid [1 ]
机构
[1] Lanzhou Jiaotong Univ, Sch Automat & Elect Engn, Lanzhou 730070, Peoples R China
基金
中国国家自然科学基金;
关键词
smart meter; electricity theft; neural networks; arduino; line losses;
D O I
10.3390/su15064868
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
相关论文
共 54 条
[1]   Electricity theft whistleblowing feasibility in commercial accommodation facilities [J].
Adongo, Charles Atanga ;
Taale, Francis ;
Bukari, Shaibu ;
Suleman, Shafic ;
Amadu, Iddrisu .
ENERGY POLICY, 2021, 155
[2]   Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks [J].
Aldegheishem, Abdulaziz ;
Anwar, Mubbashra ;
Javaid, Nadeem ;
Alrajeh, Nabil ;
Shafiq, Muhammad ;
Ahmed, Hasan .
IEEE ACCESS, 2021, 9 :25036-25061
[3]  
Anjum D., 2021, SUKKUR HAS HIGHEST N
[4]  
[Anonymous], 2019, SMART MET ADV DIS LO
[5]  
[Anonymous], DEV IND SMART EN MET
[6]  
[Anonymous], 2022, EXPRESS TRIBUNE 0425
[7]  
[Anonymous], 2020, MOBILE DEV 0810
[8]  
[Anonymous], 2022, WIKIPEDIA
[9]  
[Anonymous], PAK EL CONS TOT EC I
[10]  
[Anonymous], EL SUBS WELF AN PAK