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dc.contributor.authorCeylan, Oğuzhan
dc.date.accessioned2020-07-03T10:07:04Z
dc.date.available2020-07-03T10:07:04Z
dc.date.issued2020
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.urihttps://doi.org/10.1007/s00521-020-05062-8en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3000
dc.description.abstractRenewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient usage of cascaded H-bridge multilevel inverters (MLIs) for power control applications becomes vital for sustainable electricity. Conventionally, selective harmonic elimination equations need to be solved for obtaining optimum switching angles of MLIs. The objective of this study is to obtain switching angles for MLIs to minimize total harmonic distortion. This study contributes to the solution of this problem by utilizing two recently developed intelligent optimization algorithms: multi-verse optimization algorithm and salp swarm algorithm. Moreover, well-known particle swarm optimization is utilized for MLI optimization problem. Seven-level, 11-level and 15-level MLIs are used to minimize total harmonic distortions. Simulation results with different modulation indexes for seven-, 11- and 15-level MLIs are calculated and compared in terms of the accuracy and solution quality. Numerical calculations are verified by using MATLAB/Simulink-based models.en_US
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.subjectMultilevel invertersen_US
dc.subjectHarmonic eliminationen_US
dc.subjectMulti-verse optimization algorithmen_US
dc.subjectSalp swarm algorithmen_US
dc.subjectParticle swarm optimizationen_US
dc.titleMulti-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel invertersen_US
dc.typeArticleen_US
dc.relation.journalNeural Computing & Applicationsen_US
dc.identifier.wosWOS:000541389000003en_US
dc.identifier.doi10.1007/s00521-020-05062-8en_US
dc.contributor.khasauthorCeylan, Oğuzhanen_US


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