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العنوان
EVALUATING THE DIELECTRIC STRENGTH OF
ELASTOMERS EXPERIMENTALLY AND BY USING
ARTIFICIAL INTELLIGENCE TECHNIQUE \
المؤلف
Sheta,Tamer Mohamed Ali Ali
هيئة الاعداد
باحث / تامر محمد علي علي شتا
مشرف / سليمان محمد سليمان الدبيكي
مشرف / لؤي سعد الدين نصرت
مناقش / عادل صدقي عمارة
تاريخ النشر
2020.
عدد الصفحات
100p.:
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/1/2020
مكان الإجازة
جامعة عين شمس - كلية الهندسة - كهربة قوى
الفهرس
Only 14 pages are availabe for public view

from 117

from 117

Abstract

The epoxy and rubber are polymeric materials which nowadays became widely
used as insulating materials for many reasons such as; better electrical and
mechanical strengths, economical and ease of fabrication and maintenance. All
these benefits contributed in producing polymeric materials in various shapes
and designs for indoor and outdoor insulators applications with reasonable
mechanical and electrical properties.
Ethylene propylene diene monomer (EPDM) is one of the composite materials
that have promising electrical properties such as the electric resistivity and the
dielectric strength determined by different tests, compared with other
composite materials. By adding different filler percentages, the physical,
electrical and mechanical properties of the composite material are enhanced.
EPDM composites are one of the best polymeric insulator composites which
are widely used for cables insulation nowadays. In order to study the electrical
properties for EPDM composite as cables insulation, Alumina Trihydrate (ATH)
filler with different percentages are added to EPDM to enhance its mechanical,
electrical and thermal properties when used under different climate conditions
such as, dry, wet and salt conditions. Thus, tests have been carried out in the
present work to investigate the dielectric strength for EPDM composites with
various ATH loading. Further, the dielectric strength of EPDM composites have
been calculated using an artificial neural network based on an investigated
mathematical model. Then, the breakdown voltage for composite insulators has been calculated
using artificial neural network by the mathematical model under different
climate conditions. Consequently, the results have been compared with
experimental results, with the least error calculation. The best or the more
reasonable composite suitable for conditions could be chosen.