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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Omar Muhammed Neda
Sunni Diwan Endowment
Computer Science & IT Electrical & Electronics Engineering

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Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance Omar Muhammed Neda
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6111-6121

Abstract

Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB.
Optimal distributed generation placement using artificial intelligence for improving active radial distribution system Fadhel A. Jumaa; Omar Muhammed Neda; Mustafa A. Mhawesh
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2949

Abstract

There are several profits of distributed generator (DG) units which are believed for improving the safety of the distribution power grids. However, these profits can be maximized by ensuring optimum sizing and positioning of DG units because an arbitrary location of DG units may adversely affect and jeopardize power grids which could contribute to maximising of power loss and degradation of the voltage profile. Therefore, several approaches were suggested to ensure optimum position and size of DGs. The primary aim of this article is for establishing technique for optimum scheduling and operating of DG to lessen power loss, revamp voltage profile and overall network reliability. Artificial intelligence method called particle swarm optimization (PSO) is utilized for finding the best site and size of DG to lessen power loss and boost the voltage profile. In this paper, IEEE 33 distribution system is utilized to display applicability of PSO. The results of the PSO are compared with the results gotten by other methods in the literature. Finally, the results show that the PSO is superior than the other methods.
Review of mobile robots obstacle avoidance, localization, motion planning, and wheels Mustafa A. Mhawesh; Zaid H. Al-Tameemi; Omar Muhammed Neda
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp768-776

Abstract

The main objective of this research is to study the obstacle avoidance, Monte Carlo Localization (MCL) method, motion planning in dynamic networks for mobile robots, and mobile robots wheels depending on the previous published researches. The researchers had done their experiments on different mobile robots and had validated them. This research helps the readers to learn how the robot changes its directions to prevent itself from collisions depending on three ultrasonic sensors. Also, they will learn the localization of the mobile robots depending on the recorded data from RHINO and MINERVA robots. In addition to learning the obstacle avoiding and the localization of mobile robots, the readers will learn new planning framework. Furthermore, they will get knowledge in types of mobile robots wheels.
Co-Authors Fadhel A. Jumaa Mustafa A. Mhawesh Zaid H. Al-Tameemi