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Analysis of Stealing Electricity Based on Outliers

Published: 31 July 2024 Publication History

Abstract

The successful prevention of power theft is critical to the growth of electric power providers and society in general. Experts and academics have offered numerous power theft detection systems at this time, but there is still a lack of reliable and practical power theft prevention solutions when considering cost and practical application impacts. To put it another way, installing special power theft detection devices or data acquisition devices for users, and then using real-time differential comparison of the high-voltage side and the user side of the collected data to complete the user's real-time monitoring approach, has a high degree of science. But because theft users are after all a few, if only because of a small number of theft users and the jurisdiction of large-scale users to install special information collection device, the entire detection system operating costs will increase exponentially. Therefore, efficient and practical anti-theft measures need to take into account the efficiency of electricity theft detection and the overall cost of building detection systems. Due to the limited number of users of electricity theft, and the occurrence of electricity theft is bound to exist in the amount of electrical abnormalities. In this chapter, we propose a weighted LOF algorithm for detecting electricity theft based on outlier detection technology for large-scale electricity users. This method is better for the rapid screening of suspected users from the large-scale user group, providing directional guidelines for further audits and improving the efficiency of electricity theft detection in the electricity consumption information collection system.

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PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
January 2024
969 pages
ISBN:9798400716638
DOI:10.1145/3674225
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 31 July 2024

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