A Multi Regression Approach For Predicting The Age Of Transformer Oil In Thermal/Electrical Degradation, e-ISSN 2278-0181

Citation:

Boudraa, S., and Mokhnache Leila. 2013. “A Multi Regression Approach For Predicting The Age Of Transformer Oil In Thermal/Electrical Degradation, e-ISSN 2278-0181”. International Journal of Engineering Research & Technology Vol 2 (Issue 5 ) : pp. 1004-1008.

Abstract:

Power transformers are considered capital investments in the infrastructure of every power system in the world. They are the heart of electric power distribution and transmission systems, and it is essential that they function properly. It is knowing that insulating fluid is highly used in a composite or impregnated dielectric system with reference to both dielectric strength and ease of contamination, many investigators have devoted great efforts to investigate fluidsbehaviours under operating conditions.The objective of this study is to examine the gassing tendency under electrical discharge of aged and unaged mineral oil, and analyze the parameters in degradation, with the aim to see the most significant parameters which can really presents the degradation severity.In this regard, various scenarios were considered. The study was carried on new and aged oil submitted electrical field. The 6802, 6181 and 924 tests are used in measure of parameters in degradation.In this work, the water contents, Interfacial tension, and gas pressure generated,are considered as a parametersignificant of degradation process.These parameters were used as inputsin approach model for prediction the transformer age (life), the result show that the water can be used for detection of early stage of degradation

Publisher's Version

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Laboratoire des Systèmes de Traction Électriques

مخبر أنظمة الجر الكهربائية

A Multi Regression Approach For Predicting The Age Of Transformer Oil In Thermal/Electrical Degradation, e-ISSN 2278-0181

Citation:

Abstract:

Recent Publications

Robust Wheel Slip for Vehicle Anti-lock Braking System with Fuzzy Sliding Mode Controller (FSMC)

Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Two Types of Fuzzy Logic Controllers for the Speed Control of the Doubly-Fed Induction Machine