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Eslami M.,National University of Malaysia | Shareef H.,National University of Malaysia | Khajehzadeh M.,Islamic Azad University at Anar | Mohamed A.,National University of Malaysia
Research Journal of Applied Sciences, Engineering and Technology | Year: 2012

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms. © Maxwell Scientific Organization, 2012. Source


Eslami M.,National University of Malaysia | Shareef H.,National University of Malaysia | Mohamed A.,National University of Malaysia | Khajehzadeh M.,Islamic Azad University at Anar
International Review of Electrical Engineering | Year: 2010

In this study, the application and performance comparison of particle swarm optimization (PSO) and Genetic algorithms (GA) optimization methods, for power system stabilizer (PSS) design is presented. Recently, GA and PSO methods have attracted considerable attention among different modern heuristic optimization methods. The GA has been popular in academia and the industry, mostly because of its intuitiveness, ease of implementation, and the capability to efficiently solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO method is a relatively new heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but utilize different strategies and computational effort, it is appropriate to compare their performance. The design objective is to increase the power system stability. The design problem of the PSS parameters is formulated as an optimization problem and both PSO and GA optimization methods are used to search for optimal PSS parameters. The two-area multi-machine power system, under a wide range of system configurations and operation conditions is investigated to illustrate the performance of the both PSO and GA. The performance of both optimization methods is compared with the conventional power system stabilizer (CPSS) in terms of parameter accuracy and computational time. The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the methods in optimal tuning of PSS, to enhance power system stability. © 2010 Praise Worthy Prize S.r.l.- All rights reserved. Source


Eslami M.,Islamic Azad University at Anar | Shareef H.,National University of Malaysia | Mohamed A.,National University of Malaysia | Khajehzadeh M.,Islamic Azad University at Tehran
Przeglad Elektrotechniczny | Year: 2011

In this study, simultaneous coordinated designing of power system stabilizer and static VAR compensator damping controller is investigated. The particle swarm optimization (PSO) is used to search for optimal controller parameters, by incorporating chaos. PSO with chaos is hybridized to form a chaotic PSO, which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. The efficiency of the proposed controllers is exhibited through the eigenvalue analysis and nonlinear time-domain simulation. The results of these studies show that the proposed coordinated controllers have an excellent capability in damping interarea oscillations. Source


Eslami M.,Islamic Azad University at Anar | Shareef H.,National University of Malaysia | Mohamed A.,National University of Malaysia
International Review of Electrical Engineering | Year: 2011

This paper proposes a novel optimization technique for simultaneous coordinated designing of power system stabilizer (PSS) and static VAR compensator (SVC) as a damping controller in the multi-machine power system. PSO and chaos theory is hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. The coordinated design problem of PSS and SVC controllers over a wide range of loading conditions are formulated as a multi-objective optimization problem which is the aggregation of the two objectives related to the damping ratio and damping factor. The proposed damping controllers are tested on a weakly connected power system. The effectiveness of the proposed controllers is demonstrated through the eigenvalue analysis and nonlinear time-domain simulation. The results of these studies show that the proposed coordinated controllers have an excellent capability in damping power system inter- area oscillations and enhance greatly the dynamic stability of the power system. Moreover, it is superior to both the manually coordinated stabilizers of the PSS and the SVC damping controller. © 2011 Praise Worthy Prize S.r.l. - All righs reserved. Source


Eslami M.,Islamic Azad University at Kerman | Shareef H.,National University of Malaysia | Taha M.R.,National University of Malaysia | Khajehzadeh M.,Islamic Azad University at Anar
International Journal of Electrical Power and Energy Systems | Year: 2014

An adaptive particle swarm optimization based on nonlinear time-varying acceleration coefficients (NTVAC-PSO) is proposed for solving global optimization problems and damping of power system oscillations. The new method aims to control the global exploration ability of the original PSO algorithm and to increase its convergence rate with an acceptable solution in less iteration. A set of 10 well-known benchmark optimization problems is utilized to validate the performance of the NTVAC-PSO as a global optimization algorithm and to compare with similar methods. The numerical experiments show that the proposed algorithm leads to a significantly more accurate final solution for a variety of benchmark test functions faster. In addition, the simultaneous coordinated design of unified power flow controller-based damping controllers is presented to illustrate the feasibility and effectiveness of the new method. The performance of the proposed algorithm is compared with other methods through eigenvalue analysis and nonlinear time-domain simulation. The simulation studies show that the controllers designed using NTVAC-PSO performed better than controllers designed by other methods. Moreover, experimental results confirm superior performance of the new method compared with other methods. © 2013 Elsevier Ltd. All rights reserved. Source

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