Selective Harmonic Elimination in Multilevel Inverters Using the Bonobo Optimization Algorithm

Multilevel inverters play a crucial role in energy conversion and power electronics applications. However, the harmonics generated during the operation of such inverters can harm electrical grids and reduce system efficiency. Therefore, effectively controlling and eliminating harmonics is critical to enhancing the performance of multilevel inverters. This study focuses on achieving selective harmonic elimination (SHE) in multilevel inverters using the Bonobo Optimization Algorithm (BO). The BO algorithm draws inspiration from the social behaviors of bonobo monkeys and is an artificial intelligence algorithm. It employs evolutionary approaches to solve complex problems and offers a population-based approach to optimizing the target function. The application of the BO algorithm for seven- and eleven-level inverters is compared to the genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrate that BO provides a more effective harmonic elimination solution than GA and PSO. With this algorithm, it becomes possible to maintain harmonic levels below a specified threshold while improving the overall performance and efficiency of the inverter. In conclusion, this study successfully applies the BO algorithm for selective harmonic elimination in multilevel inverters, contributing significantly to future energy conversion and power electronics research. This approach has the potential to assist in making energy systems cleaner, more reliable, and more efficient.