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Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

Authors :
Pamir
Nadeem Javaid
Saher Javaid
Muhammad Asif
Muhammad Umar Javed
Adamu Sani Yahaya
Sheraz Aslam
Source :
Energies, Vol 15, Iss 8, p 2778 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

Details

Language :
English
ISSN :
19961073
Volume :
15
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Energies
Publication Type :
Academic Journal
Accession number :
edsdoj.505195e6a3f94f1e975f152cad37b8a7
Document Type :
article
Full Text :
https://doi.org/10.3390/en15082778