Conditional probability‐based interpretation of dissolved gas analysis for transformer incipient faults-Reference-Cited by-同舟云学术

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Conditional probability‐based interpretation of dissolved gas analysis for transformer incipient faults

Published:2017-03 Issue:4 Volume:11 Page:943-951
ISSN:1751-8695
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Taha Ibrahim B.M.¹²,Mansour Diaa‐Eldin A.¹,Ghoneim Sherif S.M.²³,Elkalashy Nagy I.⁴

Affiliation:

1. Electrical Power and Machines Engineering Department, Faculty of EngineeringTanta University31521 SeperbayTantaEgypt

2. Electrical Engineering Department, College of EngineeringTaif UniversityTaifSaudi Arabia

3. Electrical Department, Faculty of Industrial EducationSuez University43527SuezEgypt

4. Electrical Engineering Department, Faculty of EngineeringMinoufiya UniversityShebinElkom32511Egypt

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2016.0886

Reference33 articles.

1. Review of modern diagnostic techniques for assessing insulation condition in aged transformers

2. A review of faults detectable by gas-in-oil analysis in transformers

3. Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases

4. Mansour D.A.: ‘A new graphical technique for the interpretation of dissolved gas analysis in power transformers’.2012 Annual Report Conf. on Electrical Insulation and Dielectric Phenomena (CEIDP) Montreal Canada 2012 pp.195–198

5. Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases;Mansour D.A.;IEEE Trans. Dielectr. Electr. Insul.,2015

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2. Adsorption and Sensing Mechanism of Late Transition Metal Doped MoTe₂ Monolayers for Dissolved Gas Analysis in Transformer Oil: A First-Principles Study;IEEE Sensors Journal;2024-04-01

3. Optimization of Power Transformer Defect Recognition Algorithm with Dga Based on Probabilistic Neural Network;2024 International Russian Smart Industry Conference (SmartIndustryCon);2024-03-25

4. Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer;The Scientific Bulletin of Electrical Engineering Faculty;2023-12-01

5. Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis;IEEE Transactions on Dielectrics and Electrical Insulation;2023-10

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