Damping of power system oscillations using genetic algorithm and particle swarm optimization

Mahdiyeh Eslami, Hussein Shareef, Azah Mohamed, Mohammad Khajehzadeh

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2745-2753
Number of pages9
JournalInternational Review of Electrical Engineering
Volume5
Issue number6
Publication statusPublished - Nov 2010

Fingerprint

Particle swarm optimization (PSO)
Damping
Genetic algorithms
System stability
Systems engineering
Mechanics
Tuning
Industry

Keywords

  • Design PSS
  • Genetic algorithm
  • Multi-objective optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Damping of power system oscillations using genetic algorithm and particle swarm optimization. / Eslami, Mahdiyeh; Shareef, Hussein; Mohamed, Azah; Khajehzadeh, Mohammad.

In: International Review of Electrical Engineering, Vol. 5, No. 6, 11.2010, p. 2745-2753.

Research output: Contribution to journalArticle

Eslami, M, Shareef, H, Mohamed, A & Khajehzadeh, M 2010, 'Damping of power system oscillations using genetic algorithm and particle swarm optimization', International Review of Electrical Engineering, vol. 5, no. 6, pp. 2745-2753.
Eslami, Mahdiyeh ; Shareef, Hussein ; Mohamed, Azah ; Khajehzadeh, Mohammad. / Damping of power system oscillations using genetic algorithm and particle swarm optimization. In: International Review of Electrical Engineering. 2010 ; Vol. 5, No. 6. pp. 2745-2753.
@article{9e74cfa13f564a3da637915c40c16684,
title = "Damping of power system oscillations using genetic algorithm and particle swarm optimization",
abstract = "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.",
keywords = "Design PSS, Genetic algorithm, Multi-objective optimization, Particle swarm optimization",
author = "Mahdiyeh Eslami and Hussein Shareef and Azah Mohamed and Mohammad Khajehzadeh",
year = "2010",
month = "11",
language = "English",
volume = "5",
pages = "2745--2753",
journal = "International Review of Electrical Engineering",
issn = "1827-6660",
publisher = "Praise Worthy Prize",
number = "6",

}

TY - JOUR

T1 - Damping of power system oscillations using genetic algorithm and particle swarm optimization

AU - Eslami, Mahdiyeh

AU - Shareef, Hussein

AU - Mohamed, Azah

AU - Khajehzadeh, Mohammad

PY - 2010/11

Y1 - 2010/11

N2 - 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.

AB - 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.

KW - Design PSS

KW - Genetic algorithm

KW - Multi-objective optimization

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=79955025676&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955025676&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:79955025676

VL - 5

SP - 2745

EP - 2753

JO - International Review of Electrical Engineering

JF - International Review of Electrical Engineering

SN - 1827-6660

IS - 6

ER -