An Extensive Investigation on Intelligent-Based Control Techniques for the Performance Improvement in Multilevel Inverters

Multilevel inverters have become an essential technology in modern power electronics, offering benefits like reduced harmonic distortion with lower electromagnetic interference and enhanced efficiency applicable to electric vehicles, industrial drives, and renewable energy systems. Various considerations, such as fault tolerance, harmonic mitigation, and switching techniques, complicate the control of multilevel inverters. Soft computing approaches like fuzzy logic, artificial neural networks, and bio-inspired optimization techniques have shown promise in addressing these challenges. Recent developments have highlighted the significance of intelligent computational approaches in improving the inverter’s efficiency and recent optimization techniques using stellar motions are proposed and compared to eliminate selective harmonics in cascaded H-bridge multilevel inverters. Among these, big bang and big crunch outperform the other optimization techniques in terms of reduction in THD. This work will delve into the application of soft computing techniques in various sectors, thereby enabling researchers and industrial professionals to know the current trends adopted in multilevel inverters.