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

M Elsisi, MQ Tran, K Mahmoud, DEA Mansour… - Measurement, 2022 - Elsevier
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 …

Reliable estimation for health index of transformer oil based on novel combined predictive maintenance techniques

M Badawi, SA Ibrahim, DEA Mansour… - IEEE …, 2022 - ieeexplore.ieee.org
Transformer oil insulation condition may be deteriorated due to electrical and thermal faults,
which may lead to transformer failure and system outage. In this regard, the first part of this …

[HTML][HTML] Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

RA Prasojo, MAA Putra, ME Apriyani… - Electric Power Systems …, 2023 - Elsevier
Power transformers are considered one of the power system's most critical and expensive
assets. In this regard, it is vital to assess the fault within the power transformer considering …

Enhancing diagnostic accuracy of transformer faults using teaching-learning-based optimization

SSM Ghoneim, K Mahmoud, M Lehtonen… - Ieee …, 2021 - ieeexplore.ieee.org
The early detection of the transformer faults with high accuracy rates guarantees the
continuous operation of the power system networks. Dissolved gas analysis (DGA) is a …

Reliable IoT paradigm with ensemble machine learning for faults diagnosis of power transformers considering adversarial attacks

MN Ali, M Amer, M Elsisi - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Power transformer represents an important equipment in electric power systems.
Transformers are not only a source of power outages for electric utilities, but they also affect …

Optimal ratio limits of rogers' four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach

IBM Taha, A Hoballah… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The prediction of fault type in transformers at an early stage is an important aspect for power
system reliability. Checking the transformer status begins with the dissolved gas analysis …

Power transformer fault diagnosis considering data imbalance and data set fusion

Y Zhang, HC Chen, Y Du, M Chen, J Liang, J Li… - High …, 2021 - Wiley Online Library
Improving the accuracy of transformer dissolved gas analysis is always an important
demand for power companies. However, the requirement for large numbers of fault samples …

[HTML][HTML] A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

L Hong, Z Chen, Y Wang, M Shahidehpour, M Wu - Energy Reports, 2022 - Elsevier
Abstract International Electrotechnical Commission (IEC) proposed the IEC three-ratio
method based on Dissolved Gas Analysis (DGA), which is one of the most effective tools for …

Discernment of transformer oil stray gassing anomalies using machine learning classification techniques

MK Ngwenyama, MN Gitau - Scientific Reports, 2024 - nature.com
This work examines the application of machine learning (ML) algorithms to evaluate
dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed …

Transformer fault diagnosis based on improved deep coupled dense convolutional neural network

Z Li, Y He, Z Xing, J Duan - Electric Power Systems Research, 2022 - Elsevier
The normal operation of the power transformer guarantees the safety and reliability of the
power system. However, the data of gas in oil exists the phenomenon of insufficient and …