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العنوان
Adaptive protection method for smart microgrids in distribution networks /
المؤلف
Abd El-Latif, Ahmed Ibrahim Ibrahim.
هيئة الاعداد
باحث / أحمد إبراهيم إبراهيم عبداللطيف
مشرف / محمود صابر قنديل
مشرف / أحمد السيد حسن
مشرف / أحمد يوسف حتاتة
مناقش / فهمى متولى أحمد بنداري
مناقش / مجدى محمد على السعداوى
الموضوع
Electrical Engineering. Smart grid. Distributed generation.
تاريخ النشر
2018.
عدد الصفحات
140 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/1/2018
مكان الإجازة
جامعة المنصورة - كلية الهندسة - Department of Electrical Engineering
الفهرس
Only 14 pages are availabe for public view

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from 140

Abstract

As DG penetration is being considered by many distribution utilities, there is arising need to address many incompatibility issues which put a big emphasis on the need to review and implement new suitable protection schemes. A new model for Auto Recloser device which depends mainly on the over current protection is proposed and tested by exposing it for different cases of faults with different types and the results are verified. An adaptive protection technique is proposed which mainly depends on the pre-study of the electrical network and try to eliminate the failure cases that may happen in the protection system due to integration of the DG source. Then, a new adaptive code is proposed as a platform for adaptive protection device that has the ability to adjust its own setting to cope with the status of the electrical network with the help of some communication and measuring sensors. The results of implementing such model was very successful and competitive with many of the new rising solutions when tested and verified on two different standard IEEE test systems which are 13 node and 34 node test feeders. Grey wolf optimization technique is applied to the proposed model to help in optimizing the protection system parameters and shrinking the total responding time of the protection relays to the faults detected and eliminate the chance of failure occurrence. Optimization results are shown and compared with results before applying optimization, an improvement in the results is proven, and a speed in finding results and accuracy are obtained.