Harmonic Reduction in Hybrid Cascaded Multilevel Inverter Using Modified Grey Wolf Optimization

Abstract

This article proposes a modified grey wolf optimization (MGWO) which has improved rate of convergence and avoids local optima stagnation. This is achieved by improving the exploration-exploitation balance in conventional grey wolf optimization (GWO). Moreover, the use of weighted-distance strategy allows quick identification of the global optima in the proposed MGWO. This optimization is then applied for the control of a three-phase, 11-level hybrid cascaded multilevel inverter (HC-MLI). Selective harmonic elimination pulsewidth modulation technique is implemented through MGWO which generates optimal switching angles for the HC-MLI so as to eliminate lower order harmonics such as 5th, 7th, 11th, and 13th from the output voltage. In MGWO optimized HC-MLI, balancing of capacitor voltage is made possible at higher modulation index by making use of the available redundant switching states. The performance of the proposed work is validated through simulation and experimentation on a 1.5 kW prototype. The results obtained through simulation show that the MGWO algorithm is more efficient and accurate than other reported algorithms such as GWO, genetic algorithm, and particle swarm optimization in terms of performance, harmonic reduction, and convergence rate.