A.Y. Abdelaziz

Work place: Electrical Power and Machines Department, Ain Shams University, Cario, Egypt

E-mail: almoatazabdelaziz@hotmail.com


Research Interests: Computer systems and computational processes, Artificial Intelligence, Planning and Scheduling


Almoataz Y. Abdelaziz was born in Cairo, Egypt, on 1963. He received the B. Sc. and M. Sc. degrees in electrical engineering from Ain Shams University, Cairo, Egypt in 1985, 1990 respectively and the Ph. D. degree in electrical engineering according to the channel system between Ain Shams University, Egypt and Brunel University, England in 1996. He is currently a professor of electrical power engineering in Ain Shams University. He was the head of consultant engineers of the electrical group in the General Directorate for Projects & Maintenance, King Saud University, Riyadh, KSA from 2005 to 2007.

His research interests include the applications of artificial intelligence to power systems and protection and new optimization techniques in power systems operation and planning. He has authored or coauthored more than 110 refereed journal and conference papers. Dr. Abdelaziz is a member of the editorial board and a reviewer of technical papers in several journals. He is also a member in IET and the Egyptian Sub-Committees of IEC and CIGRE`.

Dr. Abdelaziz has been awarded Ain Shams University Prize for distinct researches in 2002 and for international publishing in 2010, 2011.

Author Articles
A Hybrid Wavelet-ANN-Based Protection Scheme for FACTS Compensated Transmission Lines

By A.Y. Abdelaziz Amr M. Ibrahim

DOI: https://doi.org/10.5815/ijisa.2013.07.04, Pub. Date: 8 Jun. 2013

This paper proposes an approach for the protection of transmission lines with FACTS based on Artificial Neural Networks (ANN) using Wavelet Transform (WT). The required features for the proposed algorithm are extracted from the measured transient current and voltage waveforms using discrete wavelet transform (DWT). Those features are employed for fault detection and faulted phase selection using ANN. The type of FACTS compensated transmission lines is the Thyristor-Controlled Series Capacitor (TCSC). System simulation and test results indicate the feasibility of using neural networks using wavelet transforms in the fault detection, classification and faulted phase selection of FACTS compensated transmission lines.

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Protection of Thyristor Controlled Series Compensated Transmission Lines using Support Vector Machine

By A.Y. Abdelaziz Amr M. Ibrahim

DOI: https://doi.org/10.5815/ijisa.2013.05.02, Pub. Date: 8 Apr. 2013

Recently, series compensation is widely used in transmission. However, this creates several problems to conventional protection approaches. This paper presents overcurrent and distance protection schemes, for fault classification in transmission lines with thyristor controlled series capacitor (TCSC) using support vector machine (SVM). The fault classification task is divided into four separate subtasks (SVMa, SVMb, SVMc and SVMg), where the state of each phase and ground is determined by an individual SVM. The polynomial kernel SVM is designed to provide the optimal classification conditions. Wide variations of load angle, fault inception angle, fault resistance and fault location have been carried out with different types of faults using PSCAD/EMTDC program. Backward faults have also been included in the data sets. The proposed technique is tested and the results verify its fastness, accuracy and robustness.

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Static Security Enhancement and Loss Minimization Using Simulated Annealing

By A.Y. Abdelaziz S. F. Mekhamer M. A. L. Badr H. M. Khattab

DOI: https://doi.org/10.5815/ijisa.2013.04.03, Pub. Date: 8 Mar. 2013

This paper presents a developed algorithm for optimal placement of thyristor controlled series capacitors (TCSC’s) for enhancing the power system static security and minimizing the system overall power loss. Placing TCSC’s at selected branches requires analysis of the system behavior under all possible contingencies. A selective procedure to determine the locations and settings of the thyristor controlled series capacitors is presented. The locations are determined by evaluating contingency sensitivity index (CSI) for a given power system branch for a given number of contingencies. This criterion is then used to develop branches prioritizing index in order to rank the system branches possible for placement of the thyristor controlled series capacitors. Optimal settings of TCSC’s are determined by the optimization technique of simulated annealing (SA), where settings are chosen to minimize the overall power system losses. The goal of the developed methodology is to enhance power system static security by alleviating/eliminating overloads on the transmission lines and maintaining the voltages at all load buses within their specified limits through the optimal placement and setting of TCSC’s under single and double line outage network contingencies. The proposed algorithm is examined using different IEEE standard test systems to shown its superiority in enhancing the system static security and minimizing the system losses.

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