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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2021

20-06-2020 | Original Article

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Author: Oğuzhan Ceylan

Published in: Neural Computing and Applications | Issue 6/2021

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Abstract

Renewable 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.
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Metadata
Title
Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters
Author
Oğuzhan Ceylan
Publication date
20-06-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 6/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05062-8

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