Design and Construction of a Smart Energy Theft Detector on Distribution Lines
Authors
Sylvester Emeka Abonyi
Dr. S E Abonyi, Department of Electrical Engineering, Nnamdi Azikiwe University, PMB 5025, Awka, Anambra, Nigeria (NG)
Emmanuel Chinagorom Nwadike
Dr. E.C Nwadike, Department of Mechanical Engineering, Nnamdi Azikiwe University, PMB 5025, Awka, Anambra, Nigeria. (NG)
Anthonia Ekene Ilechukwu
A. E. Ilechukwu, Department of Mechanical Engineering, Nnamdi Azikiwe University, PMB 5025, Awka, Anambra, Nigeria (NG)
James Akarali Obineche
J. A. Obineche, Department of Vocational Education, Nnamdi Azikiwe University, PMB 5025, Awka, Anambra, Nigeria. (NG)
Article Information
DOI: 10.51583/IJLTEMAS.2024.130605
Subject Category: Engineering
Volume/Issue: 13/6 | Page No: 31-36
Publication Timeline
Submitted: 2024-07-08
Published: 2024-07-08
Abstract
This work presents the design and construction of a smart energy theft detector on distribution lines. The escalating challenge of power theft within distribution lines necessitates innovative solutions to safeguard the integrity of power distribution networks. Atmega328-P, Arduino-uno Micro-processor was interphase with Bluetooth module to detect energy theft. Proteus professional software was deployed to determine the functionality of the smart energy theft detector. The work was tested and the result obtained showed a stand-alone system capable of detecting energy theft on different phases of the distribution network. The information sent to the utility operator’s liquid crystal display (LCD) indicates theft detected on either red, blue or yellow phase.
Keywords
Theft detector, Micro-Processor, Distribution network, Bluetooth module
Downloads
References
1. A. Abdulateef, Salami, Musse, Aibinu and Onakoya, "Electricity Theft Prediction In Low Voltage Distriution System Using Autoregress Technique," IJRET, vol. VIII, no. 4, pp. 250-254, 2012. [Google Scholar] [Crossref]
2. A. Abdulateef, Salami, Musse, Aibinu and Onakoya, "Electricity Theft Prediction In Low Voltage Distriution System Using Autoregress Technique," IJRET, vol. VIII, no. 4, pp. 250-254, 2012. [Google Scholar] [Crossref]
3. F. Dahunsi, O. Olakunle and A. Melodi, "Evolution Of Electricity Metering technologies In Nigeria," NJTD, vol. XVIII, no. 2, 2021. [Google Scholar] [Crossref]
4. I. Akwuniuwu, D. Okolo and R. Okonigene, "Automated Electricity Power Metering," in Wireless Networks, 2018. [Google Scholar] [Crossref]
5. D. Mhaske, R. Satam, S. Londhe, T. Konad and S. Kadam, "An Efficient Electricity Theft Detection Using XG Boost," IJEAST, vol. VI, no. 10, pp. 282-287, 2022. [Google Scholar] [Crossref]
6. D. Dike, U. Obiora, E. Nwokorie and B. Dike, "Minimizing Household Electricity Theft In Nigeria Using GSM Based Prepaid Meter," AJER, vol. IV, no. 1, pp. 59-69, 2015. [Google Scholar] [Crossref]
7. S. Abel, J. Tsado and O. Tola, "Electricity Theft Mitigation At Low Voltage Distribution End Using Indirect Matrix Converter," FUOYE JET, vol. VIII, no. 3, pp. 314-318, 2023. [Google Scholar] [Crossref]
8. D. Bhangari, A. Chavan, Y. Bhandare, R. Patik and K. Patil, "Automatic Electricity Theft Detection System," IJNRD, vol. VIII, no. 6, pp. 204-207, 2023. [Google Scholar] [Crossref]
9. J. Mabada, "Development Of A Realtime Electricity Meter Monitoring With Theft Alert System," IJRISS, vol. VII, no. 9, pp. 886-893, 2023. [Google Scholar] [Crossref]
10. E. Nta, K. Udofia and N. Okpura, "Development Of AN Energy Theft Detecton And Location system For Low Voltage Power Distribution Networks," JMSET, vol. IX, no. 4, pp. 15240-15249, 2022. [Google Scholar] [Crossref]
11. C. Mohan, L. Reddy, K. Selvaraj, C. Subhash, N. Sririam and D. Lohith, "Power Theft Identifier Using DNN," IJREISS, vol. XIII, no. 5, pp. 270-281, 2023 [Google Scholar] [Crossref]
12. G. Dimf, P. Kumar and V. Manju, "An Efficient Power Theft Detection Using Modified Deep Artificial Neural network," IJISAE, vol. XI, no. 1, 2023 [Google Scholar] [Crossref]
13. S. Munawar, N. Javaid, Z. Khan, N. Chaudhary, M. Raja, A. Milyani and A. Azhari, Electricity Theft Detection In Smart Grids Using A Hybrid BuGRU-BiLSTM Model With Feature Engineering-Based Preprocessing, MDPI. [Google Scholar] [Crossref]
14. S. Li, Y. Han, X. Yao, S. Yingchen, J. Wang and Q. Zhao, "Electricity Theft Detection In Power Grids With Deep Learning And Random Facts," JECE, vol. 20, 2019. [Google Scholar] [Crossref]
15. K. Bhaskar, M. Jamuna, P. Suresh and T. Chandana, "Electricity Theft Detection In Smart Grids Based On Deep Neural Networks," IJCRT, vol. XI, no. 12, pp. 507-521, 2023. [Google Scholar] [Crossref]
16. B. A. F. Times, "Energy Central News," Energy Central, 19 April 2019. [Online]. Available: https://energycentral.com/news/sustainable-approach-curbing-electricity-theft. [Accessed 31 January 2024]. [Google Scholar] [Crossref]
17. K. Steadman, "Electricity Theft In jamaica," New York, 2009. [Google Scholar] [Crossref]
18. S. Bhattacharayya, "The Electricity Act 2003: Will It Transform Indian Power Sector?," 2005. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- The Use of Truth Table, Logical Reasoning and Logic Gate in Teaching and Learning Process
- Resume Screening App using AI
- Entrepreneurship and STEM Competencies Education Among Female Estate Management Students of Polytechnics
- Sorensen Trust Based and Invasive Weed Algorithm Based Wireless Sensor Network Optimization
- Natural Language Processing (NLP)-Based Detection of Depressive Comments and Tweets: A Text Classification Approach