Red Deer Algorithm-Based Optimal Total Harmonic Distortion Minimization for Multilevel Inverters

This study offers a new Red Deer Algorithm (RDA) optimization technique for total harmonic distortion (THD) minimization in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To test the angles determined via RDA optimization, a single-phase seven-level cascade multi-level inverter with symmetrical DC sources was used in this study. The switching angles for the optimally minimized total harmonic distortion (OMTHD) were computed via RDA optimization. RDA optimization was compared to metaheuristic algorithms like the Improved Whale Optimization Algorithm (IWOA), Whale Optimization Algorithm (WOA), Sunflower Optimizer (SFO), Particle Swarm Optimization (PSO), Krill Herd (KH), Grey Wolf Optimizer (GWO), Galactic Swarm Optimization (GSA), Genetic Algorithm (GA), Fruit Fly Optimization Algorithm (FFO) and Artificial Greyctric Field Algorithm (AEFA). The RDA optimization outperformed the ten algorithms used as benchmark frameworks in this study as it can find angles with minimum THD for the modulation index in 0-1.

Automated summary

This study introduces a new Red Deer Algorithm (RDA) optimization technique for minimizing total harmonic distortion (THD) in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To evaluate the angles obtained through RDA optimization, a single-phase seven-level cascade multilevel inverter with symmetrical DC sources was utilized. The results showed that RDA optimization outperformed various metaheuristic algorithms in finding angles with minimum THD for the modulation index in the range of 0-1.