Three-Phase Multilevel Inverter Using Selective Harmonic Elimination with Marine Predator Algorithm

Abstract

In this paper, the marine predator algorithm (MPA) is proposed for solving transcendental nonlinear equations in a selective harmonic elimination technique using a multilevel inverter (MLI). It proved its suitability and supremacy over the other selective harmonic (SHE) techniques used in recent research as it has good precision, high probability of convergence, and improving quality of output voltage. The optimum values of switching angles from MPA are applied to control a three-phase 11-level MLI using cascaded H-bridge (CHB) topology to control the fundamental component and cancel the low order harmonics for all values of modulation index from 0 to 1. Analytical and simulation results demonstrate the robustness and consistency of the technique through the MATLAB simulation platform. The results obtained from simulation show that the MPA algorithm is more efficient and accurate than other algorithms such as teaching-learning-based optimization (TLBO), flower pollination algorithm (FPA), and hybrid particle swarm optimization with gray wolf optimization (PSOGWO). A prototype for a three-phase seven-level cascaded H-bridge inverter (7L-MLI-CHB) experimental setup is carried out. The output of this experimental test validated and supported the results obtained from the simulation analysis. The model of power loss of three-phase 7L-MLI-CHB using the silicon metal-oxide-semiconductor field-effect transistor (MOSFET) is obtained according to the modulation technique. Conduction and switching losses are calculated based on the experimental manufacturer data from the Si-MOSFET using the thermal model of Piecewise Linear Electrical Circuit Simulation (PLECS). Losses and output power are measured at different modulation index values based on the MPA algorithm. Finally, a design of heatsink volume is presented for this design at different temperatures.

Marine Predators Algorithm: A Review

“…Multiobjective MPA Multi-objective marine predator algorithm (MOMPA) [188] Engineering applications Original MPA An adaptive control parameter and inertia weight coefficient were combined with the MPA search agents to enhance their updating mechanism in the second stage [67] Nonlinear equations Original MPA MPA proposed for solving transcendental nonlinear equations in a selective harmonic elimination technique using a multilevel inverter (MLI) [131] Engineering benchmarks test functions…”

Elimination of Harmonics in Multilevel Inverter Using Multi-Group Marine Predator Algorithm-Based Enhanced RNN

“…In 2021, Riad et al [37] have proposed MPA as a method for solving transcendental nonlinear equations in a selective harmonic elimination technique using an MLI. Optimal SA are determined using MPA and they are used to regulate the 11-level CHBMLI.…”

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

“…Group three includes physics-based techniques involving physical laws for optimization problem solutions; a few techniques in this area are Henry gas solubility optimization [16,17], the big bang-big crunch [18,19] and gravitational search algorithms [20,21]. The final group includes swarm intelligence-based techniques used for optimization solutions; a few methods in this area are particle swarm optimization [22,23], artificial bee colonies [24,25], cuckoo searches [26,27], the marine predators algorithm [28,29] and the slime mold algorithm [30,31]. Recently, the dwarf mongoose optimization algorithm (DMOA) has been proposed, obtaining better results than standard state-of-the-art algorithms [32][33][34].…”