Fault detection and diagnosis in power transformers: a comprehensive review and classification of publications and methods

AR Abbasi - Electric Power Systems Research, 2022 - Elsevier
A challenging problem in the protection of power transformers is the fault detection and
diagnosis (FDD). FDD has an essential role in the reliability and safety of modern power …

A systematic review on imbalanced learning methods in intelligent fault diagnosis

Z Ren, T Lin, K Feng, Y Zhu, Z Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects

Z Chen, J Chen, Y Feng, S Liu, T Zhang… - Knowledge-Based …, 2022 - Elsevier
Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

[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 …

[PDF][PDF] Optimization of a 660 MW e supercritical power plant performance—a case of industry 4.0 in the data-driven operational management part 1. Thermal efficiency

WM Ashraf, GM Uddin, SM Arafat, S Afghan… - …, 2020 - publications.rwth-aachen.de
This paper presents a comprehensive step-wise methodology for implementing industry 4.0
in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired …

Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset

SM de Andrade Lopes, RA Flauzino… - Electric Power Systems …, 2021 - Elsevier
Early diagnosis of incipient faults in power transformers enables their predictive
maintenance and guarantees their proper operation. Recently, machine learning (ML) …

Adsorption of dissolved gases (CO, H2, CO2) produced by partial discharge in converter transformer oil by CuO (1, 2) and Ag2O (1, 2) clusters doped with MoTe2: A …

T Jiang, H Xie, H Wu, L Chen, M Bi, X Chen - Materials Today …, 2024 - Elsevier
Density functional theory (DFT) was used to analyze the effect of (CuO) n,(Ag 2 O) n (n= 1, 2)
cluster-doped MoTe 2 on the three gases (CO, H 2 and CO 2) adsorption performance and …

[HTML][HTML] Power transformer fault diagnosis using neural network optimization techniques

V Rokani, SD Kaminaris, P Karaisas, D Kaminaris - Mathematics, 2023 - mdpi.com
Artificial Intelligence (AI) techniques are considered the most advanced approaches for
diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional …

[HTML][HTML] 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 …

[HTML][HTML] Power transformer fault diagnosis based on dissolved gas analysis by correlation coefficient-DBSCAN

Y Liu, B Song, L Wang, J Gao, R Xu - Applied Sciences, 2020 - mdpi.com
The transformers work in a complex environment, which makes them prone to failure.
Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed …