2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) 2019
DOI: 10.1109/eiconrus.2019.8656834
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Research on Selective Harmonic Elimination Technique based on Particle Swarm Optimization

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Cited by 13 publications
(4 citation statements)
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“…In order to find more feasible solutions or multiple solutions, the PSO algorithm is applied and its results [150] are compared with the solution in Figure 8a obtained by fsolve function. The result of the first case (Case 1) obtained by the PSO algorithm is the same as the solution in Figure 8a, but there are two other different results (Case 2 and Case 3), as shown in Figure 11.…”
Section: Results Of She Implementationmentioning
confidence: 99%
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“…In order to find more feasible solutions or multiple solutions, the PSO algorithm is applied and its results [150] are compared with the solution in Figure 8a obtained by fsolve function. The result of the first case (Case 1) obtained by the PSO algorithm is the same as the solution in Figure 8a, but there are two other different results (Case 2 and Case 3), as shown in Figure 11.…”
Section: Results Of She Implementationmentioning
confidence: 99%
“…Genetic algorithms (GAs) have been used to find feasible solutions to the PPWM problem in many research areas, such as selected harmonic elimination, reduction in the line current harmonic and determination of the optimal switching angles with balanced/unbalanced dc sources [147][148][149]. The particle swarm optimization (PSO) algorithm is another powerful optimization tool for different PPWM formulations, which can realize functions such as the elimination of harmonics and minimization of the voltage/current THD [150][151][152].…”
mentioning
confidence: 99%
“…Решение системы (2) осуществляется с помощью алгоритмов поиска целевой функции, например, методом Ньютона -Рафсона, генетическими алгоритмами, методом оптимизации роя частиц и т. п. [16][17][18]. Целевая функция задается как…”
Section: цель работыunclassified
“…Bulanık mantık ve yapay sinir ağları kullanılmaktadır, ancak bu yöntemler modülasyon indeksine (M) göre sınırlıdır, bu nedenle M değerinin tüm aralığı için optimal bir çözüm sunamazlar [10]. M değerinin sınırlamasının yanı sıra, bu yöntemler daha iyi optimizasyon için genetik algoritma (GA) [11], parçacık sürü optimizasyonu (PSO) [12,13] gibi temel optimizasyon algoritmaları kullanılır. Ancak algoritmaların probleme uygun çözümü daha çabuk bulması ve kullanılan algoritma yapısının daha az parametre içermesi ve daha az karmaşık yapıya sahip olması istenir.…”
Section: Introductionunclassified