Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties

•Developing a reliable and secure IoT against transformer cyberattacks.•Introducing a deep 1D-CNN for fault classification against uncertainties.•Deep 1D-CNN is combined with the IoT for online transformer monitoring.•The introduced architecture increases the transformer life time. The distribution...

Full description

Saved in:
Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2022-02, Vol.190, p.110686, Article 110686
Main Authors: Elsisi, Mahmoud, Tran, Minh‐Quang, Mahmoud, Karar, Mansour, Diaa-Eldin A., Lehtonen, Matti, Darwish, Mohamed M.F.
Format: Article
Language:English
Subjects:
Artificial neural networks
Cyber-physic system
Cyberattack
Deep learning
Electric power distribution
Fault diagnosis
Industry 4.0
Internet of Things
IoT architecture
Monitoring
Neural networks
Power plants
Power transformer
Transformers
Uncertainties
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Developing a reliable and secure IoT against transformer cyberattacks.•Introducing a deep 1D-CNN for fault classification against uncertainties.•Deep 1D-CNN is combined with the IoT for online transformer monitoring.•The introduced architecture increases the transformer life time. The distribution of the power transformers at a far distance from the electrical plants represents the main challenge against the diagnosis of the transformer status. This paper introduces a new integration of an Internet of Things (IoT) architecture with deep learning against cyberattacks for online monitoring of the power transformer status. A developed one dimension convolutional neural network (1D-CNN), which is characterized by robustness against uncertainties, is introduced for fault diagnosis of power transformers and cyberattacks. Further, experimental scenarios are performed to confirm the effectiveness of the proposed IoT architecture. While compared to previous approaches in the literature, the accuracy of the new deep 1D-CNN is greater with 94.36 percent in the usual scenario, 92.58 percent when considering cyberattacks, and ±5% uncertainty. The proposed integration between the IoT platform and the 1D-CNN can detect the cyberattacks properly and provide secure online monitoring for the transformer status via the internet network.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2021.110686