A multiobjective tuning approach of power system stabilizers using particle swarm optimization

Labdelaoui, Hilal; Boudjema, Farès; Boukhetala, Djamel

doi:10.3906/elk-1411-200

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…In addition, the premature convergence of GA represents a major problem. Particle swarm optimization (PSO) for the design of the PSS parameters at different operating conditions is proposed in Abido (2002), Ekinci and Demiroren (2015), Labdelaoui et al (2016) and Ekinci (2016). However, the performance of the original PSO greatly depends on its parameters, and it often suffers from the problem of being trapped into the local optima and having a premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Ekinci

Demirören

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

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This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

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Section: Introductionmentioning

confidence: 99%

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Ekinci

Demirören

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

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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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Development of an FDB-Based Chimp Optimization Algorithm for Global Optimization and Determination of the Power System Stabilizer Parameters

Bakır

Kahraman²,

Temel³

et al. 2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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A multiobjective tuning approach of power system stabilizers using particle swarm optimization

Cited by 11 publications

References 20 publications

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Parameter optimization of power system stabilizers via kidney-inspired algorithm

HPA algoritması ile çok makinalı güç sistemi kararlı kılıcısı tasarımı

Development of an FDB-Based Chimp Optimization Algorithm for Global Optimization and Determination of the Power System Stabilizer Parameters

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