Optimization of power system stabilizers using participation factor and genetic algorithm

Hassan, Lokman H.; Moghavvemi, Mahmoud; Almurib, Haider A. F.; Muttaqi, Kashem M.; Ganapathy, Velappa

doi:10.1016/j.ijepes.2013.10.026

Cited by 70 publications

(35 citation statements)

References 26 publications

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“…The second way is proposed by [19] where it uses the GA and a bits control (1: PSS installed, 0:PSS not installed) to find out the location of PSS. In [3] by using PF and GA the optimal location of PSSs are determined.…”

Section: Ivobjective Functionmentioning

confidence: 99%

A Novel Method for Detecting Optimal Location and Parameters of Power System Stabilizer (PSS) Based On Intelligent Techniques

E.Saadati¹,

A.Damaki²,

S.Radman³

2015

IJAREEIE

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This paper presents a new technique to design a Power System Stabilizer (PSS) in multi-machine power system. The method is based on the Particle Swarm Optimization (PSO) algorithm for tuning PSS parameters including lead-lag compensator time constants as well as the controller gain. For evaluating the particles evolution throughout the searching process, an eigenvalue-based multi-objective function is used. The DIgSILENT is used as tool for modelling test system and programming PSO algorithm. Then by using a fuzzy approach implemented in Matlab/fuzzy toolbox the optimal number and location for PSSs specified. Two-area (four-machine 11bus) Power system is considered as the case study in this paper. Simulation results for various operating conditions prove the capability of the proposed algorithm in damping improvement of power system.

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Section: Ivobjective Functionmentioning

confidence: 99%

A Novel Method for Detecting Optimal Location and Parameters of Power System Stabilizer (PSS) Based On Intelligent Techniques

E.Saadati¹,

A.Damaki²,

S.Radman³

2015

IJAREEIE

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“…Geleneksel optimizasyon tekniklerinin lineer olmayan güç sistemleri problemlerini çözmedeki yetersizliğinden dolayı; son yıllarda genetik algoritma (genetic algorithm, GA) [9,10], tabu arama algoritması (tabu search algorithm) [1], diferansiyel evrim (differential evolution) [11,12], parçacık sürüsü optimizasyonu (particle swarm optimization, PSO) [13,14], yarasa algoritması (bat algorithm) [15,16], yapay arı kolonisi (artificial bee colony, ABC) [17,18] ve benzeri diğer sezgisel arama algoritmaları [19,20], PSS tasarım problemlerine başarıyla uygulanmıştır. Bu algoritmalardan PSO ve ABC sürü zekasının yeni üyeleridir ve bu algoritmaların basit yapısı ve kompleks optimizasyon problemlerini hızlı çözebilme yetenekleri vardır [3].…”

Section: Gi̇ri̇ş (Introduction)unclassified

HPA algoritması ile çok makinalı güç sistemi kararlı kılıcısı tasarımı

Ekinci¹,

Hekimoğlu²

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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This paper describes a novel hybrid approach based on particle swarm optimization (PSO) and artificial bee colony algorithm (ABC) called HPA technique that has powerful capabilities to discover the optimistic results for optimal design of power system stabilizer (PSS) in a multi-machine power system. For achieving optimal tuning of PSS parameters, the problem of selecting PSS parameters is converted to a simple optimization problem with an eigenvalue-based objective function and is solved by using the HPA technique. The effectiveness of the proposed HPA based-PSS design is verified through eigenvalue analysis, time domain simulations and some performance indices on a 3-machine 9-bus power system under different disturbances. The results of these studies show that the HPA algorithm is an alternative and more effective optimizer for tuning of PSS parameters and enhance greatly the dynamic stability of the power system compared with PSO and ABC. Also, the potential and superiority of the HPA algorithm over PSO and ABC in terms of computational time, convergence rate and solution quality is proved.

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“…The restrictions of traditional optimization methods were prevented by suggesting some soft computing algorithms. In Hassana et al (2014), Sebaa and Boudour (2009), genetic algorithms were applied for effective PSS tuning. Two functions in accordance with the eigenvalue analysis have been developed for shifting all closed-loop electromechanical modes in a pre-specified zone in the s-plan.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy and nondominated sorting genetic algorithm based power system stabilizer design

Guesmi

2017

Int. j. adv. appl. sci

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This study suggests a new method for online enhancement of multimachine system stability. Two steps centered on adjusting power system stabilizers (PSSs) are examined. Firstly, the PSS parameters are tuned off-line using an elitist optimization technique based on genetic algorithms symbolized by NSGAII over a large set of operating conditions. NSGAII was employed to move all electromechanical modes in a pre-specified area in the s-plan. Then, a flexible fuzzy logic-based neural network is proposed to adjust the parameters of the PSSs at any operating condition that can be outside the offline set by exploiting the off-line results. The suggested controllers are tested by using multi-machine system over some scenarios of serious faults and system configurations. Simulations results show the efficiency and sturdiness of the suggested stabilizers in enhancing the overall system dynamics in realtime at any loading condition selected arbitrarily.

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Optimization of power system stabilizers using participation factor and genetic algorithm

Cited by 70 publications

References 26 publications

A Novel Method for Detecting Optimal Location and Parameters of Power System Stabilizer (PSS) Based On Intelligent Techniques

A Novel Method for Detecting Optimal Location and Parameters of Power System Stabilizer (PSS) Based On Intelligent Techniques

HPA algoritması ile çok makinalı güç sistemi kararlı kılıcısı tasarımı

Neuro-fuzzy and nondominated sorting genetic algorithm based power system stabilizer design

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