Online electricity theft detection and prevention scheme for smart cities

Ballal, Makarand Sudhakar; Suryawanshi, H. M.; Mishra, Mahesh K.; Jaiswal, Gajanan C.

doi:10.1049/iet-smc.2020.0045

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Moreover, technology-based models have been Electricity 2024, 5 explored Yan and Wen [8], Tanwar, et al [9], Zheng, et al [10]. Regardless of these noble efforts to address electricity theft, Ballal, et al [11] stated that the challenge had reached alarming levels, as shown by the catastrophic impact that the challenge has had on people's lives and various economies across the world. It is on this basis that Mohanty, et al [12], Leninpugalhanthi, et al [13] contend for further exploration of technology-based solutions to curb the persistent electricity-theft phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Kgaphola,

Marebane,

Hans

2024

Electricity

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Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.

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Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Kgaphola,

Marebane,

Hans

2024

Electricity

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“…To protect user privacy, it is necessary to perform privacy processing on user data before publishing it to the detector server [12]. Common data privacy processing technologies such as data aggregation [13,14] have well achieved privacy-preserving data collection, but they cannot be combined with the actual application of electricity theft detection [15,16]. For example, in [13], each user encrypts his/her own electricity consumption data into a ciphertext using a lifted ElGamal homomorphic cryptosystem before sending it to the aggregator [17].…”

Section: Introductionmentioning

confidence: 99%

FPETD: Fault‐Tolerant and Privacy‐Preserving Electricity Theft Detection

Dong

Zeng

Liu

2021

Wireless Communications and Mobile Computing

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Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning-based detection may expose user privacy. In this paper, we propose a real-time fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme that combines n -source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault-tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

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Electricity Theft Detection Methods and Analysis Using Machine Learning: Overview

Kaur¹,

Saini²

2022

Lecture Notes in Electrical Engineering

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Online electricity theft detection and prevention scheme for smart cities

Cited by 6 publications

References 14 publications

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

FPETD: Fault‐Tolerant and Privacy‐Preserving Electricity Theft Detection

Electricity Theft Detection Methods and Analysis Using Machine Learning: Overview

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