M. A. El-Dessouki

Work place: Electrical Power & Machines Dept. Faculty of Engineering, Ain Shams University, Cairo, Egypt

E-mail: mdessuki@hotmail.com


Research Interests: Computational Science and Engineering, Engineering


Maher A. El-Dessouki Received his Ph.D. degree from Warsaw University of technology in 1994 in dynamicstudy of power systems considering electrical machines as dynamic loads. Now, he is an assistant professor in the Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

His research interests include modeling, simulation, control of electrical machines, power systems dynamics, power system stability, power system reconstruction, design of novel electrical machines, new techniques in power system distribution, use of the artificial intelligent in the control of both the electrical machines and power systems. He supervised many research projects. He teaches many courses of electrical machines and power system inside and outside Egypt in different universities.

Mr. El-Dessouki is a member of the Association of Energy engineers (AEE).

Author Articles
Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques

By A. Y. Abdelaziz M. A. El-Dessouki

DOI: https://doi.org/10.5815/ijisa.2013.10.01, Pub. Date: 8 Sep. 2013

Many techniques are used for Transient Stability assessment (TSA) of synchronous generators encompassing traditional time domain state numerical integration, Lyapunov based methods, probabilistic approaches and Artificial Intelligence (AI) techniques like pattern recognition and artificial neural networks.
This paper examines another two proposed artificial intelligence techniques to tackle the transient stability problem. The first technique is based on the Inductive Inference Reasoning (IIR) approach which belongs to a particular family of machine learning from examples. The second presents a simple fuzzy logic classifier system for TSA. Not only steady state but transient attributes are used for transient stability estimation so as to reflect machine dynamics and network changes due to faults.
The two techniques are tested on a standard test power system. The performance evaluation demonstrated satisfactory results in early detection of machine instability. The advantage of the two techniques is that they are straightforward and simple for on-line implementation.

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