Classification of defects and evaluation of electrical tree degradation in cable insulation using pattern recognition method and weibull process of partial discharge

2008 ◽  
Author(s):  
Seong-Hee Park ◽  
Hae-Eun Jung ◽  
Jae-Hun Yun ◽  
Byoung-Chul Kim ◽  
Seong-Hwa Kang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Partial Discharge ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Cable Insulation ◽  
Electrical Tree ◽  
Weibull Process

scholarly journals Fractal‐based autonomous partial discharge pattern recognition method for MV motors

High Voltage ◽  
2018 ◽  
Vol 3 (2) ◽  
pp. 103-114 ◽  
Author(s):  
Zhuo Ma ◽  
Yang Yang ◽  
Martin Kearns ◽  
Kevin Cowan ◽  
Huajie Yi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Partial Discharge ◽  
Discharge Pattern ◽  
Pattern Recognition Method ◽  
Recognition Method

scholarly journals Application of Pattern Recognition Method for Color Assessment of Oriental Tobacco based on HPLC of Polyphenols

2003 ◽  
Vol 20 (5) ◽  
pp. 356-359 ◽  
Author(s):  
S Dagnon ◽  
A Edreva
Keyword(s):  
Pattern Recognition ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Chlorogenic Acid ◽  
High Performance ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Additional Tool ◽  
Hplc Profiles

AbstractThe color of Oriental tobaccos was organoleptically assayed, and high performance liquid chromatography (HPLC) of polyphenols was performed. The major tobacco polyphenols (chlorogenic acid, its isomers, and rutin), as well as scopoletin and kaempferol-3-rutinoside were quantified. HPLC polyphenol profiles were processed by pattern recognition method (PRM), and the values of indexes of similarity (Is,%) between the cultivars studied were determined. It was shown that data from organoleptic color assessment and from PRM based on HPLC profiles of polyphenols of the cultivars studied are largely compatible. Hence, PRM can be suggested as an additional tool for objective color evaluation and classification of Oriental tobacco.

An efficient partial discharge pattern recognition method using texture analysis for transformer defect models

2018 ◽  
Vol 28 (7) ◽  
pp. e2558 ◽  
Author(s):  
Reza Rostaminia ◽  
Mohsen Saniei ◽  
Mehdi Vakilian ◽  
Seyyed Saeedollah Mortazavi ◽  
Vahid Parvin Darabad
Keyword(s):  
Pattern Recognition ◽  
Texture Analysis ◽  
Partial Discharge ◽  
Discharge Pattern ◽  
Pattern Recognition Method ◽  
Recognition Method
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