Asynchronous Particle Swarm Optimization-Genetic Algorithm (APSO-GA) Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 69; číslo 2; s. 1477 - 1487
Hlavní autoři: Memon, Mudasir Ahmed, Siddique, Marif Daula, Mekhilef, Saad, Mubin, Marizan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Convergence
Evolutionary algorithms
Evolutionary computation
Genetic algorithm (GA)
Genetic algorithms
Harmonic analysis
Harmonics
Inverters
Machine learning
multilevel inverter (MLI)
Multilevel inverters
Optimization
Particle swarm optimization
particle swarm optimization (PSO)
Rings (mathematics)
selective harmonic elimination pulsewidth modulation (SHEPWM)
Switches
Switching
Topology
ISSN:0278-0046, 1557-9948
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Shrnutí:In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching-learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3060645