Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

被引：1

作者：

Souza, Murilo A. ^{[1
]}

Gouveia, Hugo T. V. ^{[2
]}

Ferreira, Aida A. ^{[2
]}

de Lima Neta, Regina Maria ^{[1
]}

Neto, Otoni Nobrega ^{[1
]}

da Silva Lira, Milde Maria ^{[1
]}

Torres, Geraldo L. ^{[1
]}

de Aquino, Ronaldo R. B. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Dept Elect Engn, BR-50740550 Recife, Brazil

[2] Fed Inst Pernambuco, Dept Elect Syst, BR-50740545 Recife, Brazil

来源：

ENERGIES | 2024年 / 17卷 / 07期

关键词：

non-technical loss; distribution systems; smart grids; artificial intelligence; ELECTRICITY THEFT; CYBER-SECURITY;

D O I：

10.3390/en17071729

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

引用

页数：16

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