Multilevel inverters (MLI) are becoming more common in different power applications, such as active filters, elective vehicle drives, and dc power sources. The Multi-Group Marine Predator Algorithm (MGMPA) is introduced in this study for resolving transcendental nonlinear equations utilizing an MLI in a selective harmonic elimination (SHE) approach. Its applicability and superiority over various SHE approaches utilized in recent research may be attributed to its high accuracy, high likelihood of convergence, and improved output voltage quality. For the entire modulation index, the optimum switching angles (SA) from Marine Predator Algorithm (MPA) is utilized to control a three-phase 11-level MLI employing cascaded H-bridge (CHB) architecture to regulate the vital element and eliminate the harmonics. The limitation of SHE is that it is difficult to find solutions for nonlinear equations. As a result, specific optimization approaches must be used. Artificial Intelligence (AI) algorithms can handle such a nonlinear transcendental equation successfully, although their time consumption as well as convergence abilities vary. Here, recurrent neural network (RNN) is considered where the hidden neurons are tuned by MGMPA with the intention of harmonic distortion parameter (HDP) minimization, thus called as enhanced recurrent neural network (ERNN). The method’s resilience and consistency are demonstrated by simulation and analytical findings. The MGMPA method is more effective and appropriate than various algorithms including the MPA, Harris Hawks optimization (HHO), and Whale optimization algorithm (WOA), according to simulation data.

Considering present shortage of fossil fuels and discharges of ozone harming substances, power developed from Renewable Energy Sources (RES) is identified as the excellent choice for producing the electricity. The characteristic of an inverter is to transform the dc power into ac power to fulfill out the requirements of load. Despite its advantage, the presence of harmonics in the output voltage reduces both the efficiency and the performance of the inverter. Several researches have been carried out since last three decades for eliminating the harmonics. Based upon several researches, it reveals that the Selective Harmonic Elimination Pulse-width Elimination technique (SHEPWM) has proven to be the best in eliminating lower order harmonics. But when calculus based methods are used for solving the non-linear transcendental equations, this technique has shown some complications. Artificial Intelligence (AI) techniques appear to be better in solving the above said equations. This review paper provides the performance of some AI techniques used for eliminating the harmonics in inverters. Based upon the information collected from various literatures and its results, conclusion has been made.

The broad discipline of Artificial Intelligence (AI) aims to automate processes that currently require human intelligence. Understanding intelligence and creating intelligent systems are the two clear objectives of AI. AI is interested in decision-making tools including knowledge representation, machine learning, heuristic reasoning, and inference approaches. The roots of AI can be found in philosophy, literature, and the human imagination. Early advancements in engineering, electronics, and many other fields have inspired AI. In this paper, we begin with a general introduction to the area of AI, then move on to its inception, history, subfields, and various applications.