ASO-Based SHE Method on Hybrid Multilevel Inverter for PV Application Under Dynamic Operating Conditions

In multilevel inverters (MLI), output voltage waveform consists of dominant low order harmonics, which needs to be minimized. Simultaneously, good control over the fundamental voltage for desirable operation is needed. In this paper, an atom search optimization (ASO) based selective harmonic elimination (SHE) method is proposed for a variable dc bus based reduced component count (RCC) MLI. ASO is a population-based metaheuristic algorithm, which mathematically models the motion of atoms in nature to accurately determine the optimum firing angle of the switches by solving SHE fitness function. The proposed ASO SHE method outperforms recent metaheuristics based SHE methods such as bee algorithm, imperialistic colonial algorithm (ICA), firefly, particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) in solving SHE problem for 11-level multilevel inverter. Detailed simulation case studies are presented to effectively demonstrate the performance of the proposed ASO SHE method on a stand-alone photovoltaic (PV) based RCC MLI subjected to sudden changes in irradiance, load and dc-link capacitor voltage. The experimental results on a PV based variable dc bus multilevel inverter validate the excellent performance of ASO SHE method in minimizing the total harmonic distortion (THD) and dominant order harmonics under sudden change in operating conditions.