Bhavesh Pawar,
Vaishali Baste,
Achyut khot,
Vijay Jadhav,
- Student Department of Electronics and Telecommunication Engineering, SKNCOE, SPPU, Pune Maharashtra India
- Student Department of Electronics and Telecommunication Engineering, SKNCOE, SPPU, Pune Maharashtra India
- Student Department of Electronics and Telecommunication Engineering, SKNCOE, SPPU, Pune Maharashtra India
- Student Department of Electronics and Telecommunication Engineering, SKNCOE, SPPU, Pune Maharashtra India
Abstract
With the increasing demand for efficient energy distribution and consumption monitoring in smart grid systems, the need for robust power theft detection mechanisms becomes paramount. This project proposes an IoT-based solution utilizing energy metering to detect instances of power theft within different segments of the distribution network. The system employs three types of energy meters: Distribution Point (DP), Pole, and Domestic meters. Every meter is positioned thoughtfully to track the flow of electricity at different places throughout the distribution network. DP meters monitor the energy supplied to specific areas, Pole meters monitor energy distribution to multiple domestic houses, while Domestic meters track individual energy consumption. The heart of the system lies in the integration of these meters with ESP32 microcontrollers, enabling real-time data transmission via Wi-Fi to an IoT platform such as ThingSpeak. This makes it possible to continuously monitor and analyze trends of energy utilization. Discrepancies between the readings of adjacent meters are indicative of potential power theft. By comparing the readings of DP, Pole, and Domestic meters, the system can precisely pinpoint the location of power theft within the distribution network. In case of any anomaly, such as a significant variance in readings, the system triggers alerts via an LCD display and a buzzer connected to the ESP32, providing immediate notification to the concerned authorities. This novel method of detecting power theft not only improves the effectiveness of energy distribution but also helps utility companies minimize revenue losses. Additionally, the system promotes transparency and accountability in energy consumption, ultimately leading to a more reliable and sustainable smart grid infrastructure. The detection of power theft is facilitated by comparing the readings of adjacent meters. Significant variances in readings between DP, Pole, and Domestic meters serve as indicators of potential theft occurrences. By analyzing these variations, the system can precisely identify the location of power theft within the distribution network. Upon detection of suspicious activity, the system triggers alerts via an LCD display and a buzzer connected to the ESP32 microcontroller. This immediate notification mechanism ensures prompt response from utility providers, enabling them to take necessary actions to mitigate the theft and prevent further losses.
Keywords: Internet of things, Things Speak, Electricity meters, Power Theft, ESP 32 microcontroller.
References
- Vaishali Sonawane, Sanjay B. Patil, “Study of Open Circuit Fault Diagnosis in VSI using Discrete Wavelet Transform and Machine Learning Approach”, International Journal of Advanced Science and Technology Sept2020, Vol. 29, No. 3, (2020), pp. 13370 – 13378, ISSN: 2005-4238
- Venkatesan and R. Anitha, “Smart grid implementation with IoT based power theft detection and control mechanism,” in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 253-257, 2019. DOI: [10.1109/ICACCS.2019.8722833]
- C. Paul, S. A. H. Naqvi, and A. P. Shaha, “Smart grid monitoring and power theft detection using IoT and machine learning techniques,” in 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1-6, 2018. DOI: [10.1109/PEDES.2018.8707787]
- S. Sankar and M. Nandhini, “IoT based smart grid for power theft detection and power quality monitoring,” in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI),2017.51-56, 2017. DOI: [10.1109/ICPCSI.2017.8391805]
- Gupta and S. Sharma, “An IoT-based power theft detection and monitoring system for smart grids,” in 2020 IEEE International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), pp. 1-6, 2020. DOI: [10.1109/ICSGCE48840.2020.9240852]
- Kumar and S. Singh, “Power theft detection in smart grids using IoT and machine learning algorithms,” in 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp. 177-182, 2019. DOI: [10.1109/IACC48083.2019.8970842]
- S.S.R. Depuru, “Modeling, Detection, and Prevention of Electricity Theft for Enhanced Performance and Security of Power Grid,” The University of Toledo, Aug. 2012.
- Nagi, K.S. Yap, S.K. Tiong, S.K. Ahmed, and A.M. Mohammad, “Detection of abnormalities and electricity theft using genetic support vector machines,” Proc. IEEE Region 10 Conference TENCON, Hyderabad, India, Jan. 2009, pp. 1–6.
- Sahoo, D. Nikovski, T. Muso, and K. Tsuru, “Electricity theft detection using smart meter data,” in Innovative Smart Grid Technologies Conference (ISGT), IEEE Power and Energy Society, 2015.
- A. Salinas and P. Li, “Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach,” IEEE Trans. Power Syst.,vol. 31, no. 2, pp. 883 – 894, 2016
Journal of Semiconductor Devices and Circuits
| Volume | |
| Received | July 9, 2024 |
| Accepted | July 17, 2024 |
| Published | August 4, 2024 |