Search In this Thesis
   Search In this Thesis  
العنوان
Machine Learning Techniques for mitigating voltage collapse \
المؤلف
Abd Elmaged, Mohamed Usama Shahat.
هيئة الاعداد
باحث / محمد اسامة شحات عبد المجيد
مشرف / هدى قرشى محمد
مشرف / اسلام احمد محمود احمد المداح
مناقش / رضا حسين عبد الله ابو العز
مناقش / حازم محمود عباس
تاريخ النشر
2018.
عدد الصفحات
91p.:
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/1/2018
مكان الإجازة
جامعة عين شمس - كلية الهندسة - كهرباء حاسبات
الفهرس
Only 14 pages are availabe for public view

from 115

from 115

Abstract

In the power system, the instantaneous and permanent stability is a major requirement that cannot be overlooked. Because of the power grid large-scale systems, any disturbance anywhere on the power grid could pose a reason of an overall dynamic imbalance. Major consequences could be occurred to the electricity feeding across wide areas of country which is called partial blackout, even entire country which is called overall blackout. It is perhaps for this reason that the existence voltage stability indices which indicate the power grid system stability level is very essential. knowing the voltage stability level of the transmission lines that involves the power grid in real time ( online operation) , the voltage stability of the entire power grid could be obtained easily . There are several mathematical base voltage indices. But in this proposal, another voltage stability index will be build based on the machine learning techniques to mitigate the voltage collapse phenomenon. This predictor is proposed in transient stability analysis based on machine learning techniques such as (Linear regression, neural network, and Decision tree). This predictor is built after a comparison was made between the impacts of various machine learning algorithms using different datasets. Three different mathematical voltage stability indices (FVSI, Lmn, and NLSI) had been used to prepare datasets for the training purpose. An early warning system had been built based on the proposed predictor. This early warning system could be used to inform the system operator with the hazards of voltage instability issues in the electric power grid and visualize these hazards. The E.W.M (Early warning module) had then been used as a kernel to build V.S.A.M.A (Voltage Stability Automatic Maneuver Algorithm) that can handle the voltage instability issue.
Several tools had been used to obtain and implement the V.S.A.M.A .The Power World Simulation tool had been used as a simulator of the electric power grid (IEEE 9-Bus system). The RapidMiner Studio had been used to build the predictors that can evaluate the stability of transmission lines of the electric power grid. Finally, the Matlab had been used to implement the visualizer, the voltage instability predictor and the proposed algorithm (V.S.A.M.A).