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A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

https://doi.org/10.1109/access.2021.3113592
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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inferredcites →https://doi.org/10.1109/ichqp.2016.7783346openalex-snapshot
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inferredcites →https://doi.org/10.1109/icmla.2016.0052openalex-snapshot
Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning
inferredcites →https://doi.org/10.1109/tetc.2013.2274043openalex-snapshot
CONSUMER: A Novel Hybrid Intrusion Detection System for Distribution Networks in Smart Grid
inferredcites →https://doi.org/10.1109/tii.2017.2785963openalex-snapshot
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
inferredcites →https://doi.org/10.1109/tnnls.2020.2994116openalex-snapshot
Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD
inferredcites →https://doi.org/10.1109/tpwrs.2018.2853162openalex-snapshot
NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting
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Using RFID for anti-theft in a Chinese electrical supply company: A cost-benefit analysis
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1109/access.2022.3222883openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2022.3230952openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tsg.2022.3193989openalex-snapshot
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A novel and effective method based deep learning model for detecting non-technical electricity losses
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Survey on Simulation and Vulnerability Testing in Smart Grid
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