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

Abstract

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.