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Enhanced Total Harmonic Distortion Optimization in Cascaded H-Bridge Multilevel Inverters Using the Dwarf Mongoose Optimization Algorithm.

Authors :
Salih, Sinan Q.
Mejbel, Basim Ghalib
Ahmad, B. A.
Taha, Taha A.
Bektaş, Yasin
Aldabbagh, Mohammed M.
Hussain, Abadal-Salam T.
Hashim, Abdulghafor Mohammed
Veena, B. S.
Source :
Journal of Robotics & Control (JRC); 2024, Vol. 5 Issue 6, p1862-1871, 10p
Publication Year :
2024

Abstract

Total harmonic distortion (THD) is one of the most essential parameters that define the operational efficiency and power quality in electrical systems applied to solutions like cascaded H-bridge multilevel inverters (CHB-MLI). The reduction of THD is crucial due to the fact that improving the system's power quality and minimizing the losses are key for performance improvement. The purpose of this work is to introduce a new DMO-based approach to optimize the THD of the output voltage in a three-phase nine-level CHB-MLI. The proposed DMO algorithm was also subjected to intense comparison with two benchmark optimization techniques, namely Genetic Algorithm and Particle Swarm Optimization with regards to three parameters, namely convergence rate, stability, and optimization accuracy. A series of MATLAB simulations were run to afford the evaluation of each algorithm under a modulation index of between 0.1 and 1.0. The outcome of the experiment amply proves that in comparison with THD minimization for the given OP, the DMO algorithm was significantly superior to both RSA-based GA and PSO algorithms in their ability to yield higher accuracy while requiring lesser computational time. Consequently, this work could expand the application of the DMO algorithm as a reliable and effective means of enhancing THD in CHB-MLIs as well as advancing the overall quality of power systems in different electrical power networks. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
27155056
Volume :
5
Issue :
6
Database :
Complementary Index
Journal :
Journal of Robotics & Control (JRC)
Publication Type :
Academic Journal
Accession number :
182174543
Full Text :
https://doi.org/10.18196/jrc.v5i6.23548