Hybrid CNN–Transformer Network for Electricity Theft Detection in Smart Grids

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a res...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-10, Vol.23 (20), p.8405
Main Authors: Bai, Yu, Sun, Haitong, Zhang, Lili, Wu, Haoqi
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Classification
convolutional neural network
Deep learning
Efficiency
Electric properties
Electric transformers
Electric utilities
Electricity
electricity theft detection
Investigations
Machine learning
Marketing research
Neural networks
Optimization
Smart grid technology
smart grids
Theft
Time series
transformer neural network
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Summary:Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23208405