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Ahmed M. Azmy

Researcher at Tanta University

Publications -  99
Citations -  1850

Ahmed M. Azmy is an academic researcher from Tanta University. The author has contributed to research in topics: Electric power system & Fault (power engineering). The author has an hindex of 19, co-authored 93 publications receiving 1439 citations. Previous affiliations of Ahmed M. Azmy include University of Duisburg-Essen & Kafrelsheikh University.

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Proceedings ArticleDOI

Impact of distributed generation on the stability of electrical power system

TL;DR: In this article, the performance of a power system with significant penetration of distributed resources is described to assess different types of stability of the bulk network, and different stability classes are analyzed with each penetration level of the distributed generation.
Journal ArticleDOI

Dispersion behavior and breakdown strength of transformer oil filled with TiO 2 nanoparticles

TL;DR: In this paper, the effect of surfactant concentration on dispersion behavior and agglomerate size of transformer oil-based nanofluids was investigated using optical microscope analysis, transmission electron microscope (TEM) analysis and zeta potential measurements.
Journal ArticleDOI

Improving Fault Ride-Through Capability of DFIG-Based Wind Turbine Using Superconducting Fault Current Limiter

TL;DR: The use of superconducting fault current limiter (SFCL) is proposed to reduce fault current level at the stator side and improve the fault ride-through (FRT) capability of the system.
Journal ArticleDOI

A systematic decision-making approach for planning and assessment of hybrid renewable energy-based microgrid with techno-economic optimization: A case study on an urban community in Egypt

TL;DR: A systematic and integrative decision-making approach for efficient planning and assessment of hybrid renewable energy-based microgrid systems and reveals that solar, wind, fuel cell show optimal configuration plan that yields a minimum net present and energy costs.
Journal ArticleDOI

Online optimal management of PEMFuel cells using neural networks

TL;DR: In this paper, a two-phase approach to manage the daily operation of proton exchange membrane (PEM) fuel cells for residential applications is presented, where first, a database is extracted by performing offline optimization processes at different load demands and natural gas and electricity tariffs using a GA.