Future University In Egypt (FUE)
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Altagamoa Al Khames, Main centre of town, end of 90th Street
New Cairo
Egypt
Faculty of Engineering & Technology

Moneer Mohamed Ali Abu-Elnaga

Basic information

Name : Moneer Mohamed Ali Abu-Elnaga
Title: Associate Professor
Personal Info: Moneer Mohamed Abu-Elnaga Born on May 5, 1952 - Cairo, Egypt

Education

Certificate Major University Year
PhD Electrical Engineering McMaster University, Hamilton, Ontario, Canada 1987
Masters Power and Electric Machines Engineering Ain Shams - Egypt 1981
Bachelor Electrical Engineering Ain Shams - Egypt 1975

Teaching Experience

Name of Organization Position From Date To Date
King Khalid University, Saudi Arabia. Head of Electrical Engineering Department 01/01/2008 01/01/2011
أبها – المملكة العربية السعودية جامعة الملك خالد 01/01/2004 01/01/2008
القاهرة – جمهورية مصر العربية الأكاديمية العربية للعلوم والتكنولوجيا والنقل البحري 01/01/2001 01/01/2004
القاهرة – جمهورية مصر العربية أكاديمية أخبار اليوم 01/01/2001 01/01/2004
القاهرة – جمهورية مصر العربية جامعة عين شمس 01/01/1998 01/01/2004
العين – دولة الإمارات العربية المتحدة جامعة الإمارات العربية المتحدة 01/01/1991 01/01/1997
القاهرة – جمهورية مصر العربية جامعة عين شمس 01/01/1988 01/01/1991
الولايات المتحدة الأمريكية جامعة ولاية أيوا 01/01/1987 01/01/1988
كندا جامعة ماكماستر 01/01/1983 01/01/1987
اAin Shams University, Department of Electrical Engineering, Cairo, Egypt. Teaching Assistant 01/01/1976 01/01/1982

Researches /Publications

Reliability Assessment of Grid Connected Photovoltaic Generation Systems - 01/1

Moneer Mohamed Ali Abu-Elnaga

Ahmed M. Mustafa, Yasser G. Hegazy

01/11/2015

This paper investigates the reliability of different topologies of grid connected photovoltaic (PV) systems. The topologies considered in this study are centralized topology, string topology and multi-string topology. The reliability of each topology is evaluated by using suitable reliability indices that are based on calculating the probability of different operating states. These states are evaluated using the state enumeration method which is based on computing the failure rate of each component while considering the different environmental conditions. Simulations are performed in MATLAB environment on a 58.3 KW photovoltaic system to compare between the reliability of the three topologies.

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Load Modeling Effect On Voltage Stability Of Large Scale Power Systems Using Energy Function Technique - 01/0

Moneer Mohamed Ali Abu-Elnaga

A. Y. Abdelaziz, A. M. Abdeen

01/05/2008

Steady state voltage stability analysis is effectively used to determine a stability margin that shows how close the current operating point of a power system to the voltage collapse point. The energy function technique represents a powerful method to assess voltage stability of multi-machine power systems. The sparse network formulation of this method retains the original structure of the system network and avoids network reduction. This permits the system loads to be modeled as they exist in practical life. Neglecting these models may lead to misleading results; e.g. the system appears to be stable while it is actually unstable. This paper investigates this serious problem and shows the significant effect of load modeling on power system voltage stability. A closed form expression of the energy function is obtained. The proposed technique is applied to Ontario-Hydro real power system (Canada).

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Voltage Stability Assessment of Multi-machine Power Systems using Energy Function and Neural Networks Techniques - 01/1

Moneer Mohamed Ali Abu-Elnaga

A. Y. Abdelaziz, M. A. Elsharkawy and K. M. Elbahrawy

01/12/2006

Voltage stability problems have been one of the major concerns for electric utilities as a result of heavy loading of power system. Steady state voltage stability analysis is effectively used to determine a stability margin that shows how close the current operating point of a power system to the voltage collapse point. This article presents a generalized energy function for voltage stability assessment of multi-machine power systems. The formulated energy function provides an excellent indicator of the system vulnerability to voltage collapse. It is, also, used to rank the system buses according to their contributions to voltage collapse. The proposed technique is applied to a test system and Ontario-Hydro real power system. Also, an investigation on the application of artificial neural networks (ANN) in voltage stability assessment has been developed. A multi-layer feed-forward ANN with error back-propagation learning algorithm is proposed for calculation of voltage stability margins (VSM). Extensive testing of the proposed ANN-based approach indicates its validity for determination of power system voltage collapse.

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Awards

Award Donor Date
Promoting relations between professor and students. Futur University, Cairo egypt. 2014
1st Academic Award of E-Learning King Khalid University, Saudi Arabia 2010
Research grant Research Council, UAE University 1995
Research grant Research Council, UAE University 1994
Chairman Academic Award McMaster University, Hamilton, Ontario, Canada 1985
Chairman Academic Award McMaster University, Hamilton, Ontario, Canada 1984

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