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 …
diagnosis (FDD). FDD has an essential role in the reliability and safety of modern power …
Condition monitoring techniques for electrical equipment-a literature survey
Y Han, YH Song - IEEE Transactions on Power delivery, 2003 - ieeexplore.ieee.org
Increasing interest has been seen in condition monitoring (CM) techniques for electrical
equipment, mainly including transformer, generator, and induction motor in power plants …
equipment, mainly including transformer, generator, and induction motor in power plants …
Pd-doped MoS2 monolayer: A promising candidate for DGA in transformer oil based on DFT method
Density functional theory (DFT) method was carried out to simulate the adsorption of three
dissolved gases on Pd-doped MOS 2 (Pd-MoS 2) monolayer. We initially studied the …
dissolved gases on Pd-doped MOS 2 (Pd-MoS 2) monolayer. We initially studied the …
Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application
Many hydrologic phenomena and applications such as drought, flood, irrigation
management and scheduling needs high resolution satellite soil moisture data at a …
management and scheduling needs high resolution satellite soil moisture data at a …
ANN assisted multi sensor information fusion for BLDC motor fault diagnosis
TA Shifat, JW Hur - IEEE Access, 2021 - ieeexplore.ieee.org
Multiple sensor data fusion is necessary for effective condition monitoring as the electric
machines operate in a wide range of diverse operations. This study investigates sensor …
machines operate in a wide range of diverse operations. This study investigates sensor …
Retraining strategy-based domain adaption network for intelligent fault diagnosis
Industrial Internet of Things (IIoT) obtains big data from industrial facilities. Based on these
data, health conditions for facilities can be predicted using machine learning methods, which …
data, health conditions for facilities can be predicted using machine learning methods, which …
Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis
J Faiz, M Soleimani - IEEE Transactions on Dielectrics and …, 2018 - ieeexplore.ieee.org
Transformers are vital components of power systems as they are situated between energy
generation and consumers and their failure disrupts the use of electrical energy. Therefore …
generation and consumers and their failure disrupts the use of electrical energy. Therefore …
A combined ANN and expert system tool for transformer fault diagnosis
Z Wang, Y Liu, PJ Griffin - IEEE Power Engineering Society …, 1999 - ieeexplore.ieee.org
A combined artificial neural network and expert system tool (ANNEPS) is developed for
transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). ANNEPS takes …
transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). ANNEPS takes …
Chemical sensing strategies for real-time monitoring of transformer oil: A review
C Sun, PR Ohodnicki, EM Stewart - IEEE Sensors Journal, 2017 - ieeexplore.ieee.org
Power transformers are a central component in the field of energy distribution and
transmission. The early recognition of incipient faults in operating transformers is …
transmission. The early recognition of incipient faults in operating transformers is …
Fault diagnosis of power transformer based on multi-layer SVM classifier
G Lv, H Cheng, H Zhai, L Dong - Electric power systems research, 2005 - Elsevier
Support vector machine (SVM) is a novel machine learning method based on statistical
learning theory (SLT). SVM is powerful for the problem with small sampling, nonlinear and …
learning theory (SLT). SVM is powerful for the problem with small sampling, nonlinear and …