Dandelion Optimizer and Gold Rush Optimizer Algorithm-Based Optimization of Multilevel Inverters

Abstract

With the increasing integration of renewable energy sources into distribution and transmission networks, the efficiency of cascade H-bridge multilevel inverters (MLIs) in power control applications has become increasingly significant for sustainable electricity generation. Traditionally, obtaining optimal switching angles of MLIs to minimize total harmonic distortion (THD) requires solving the selective harmonic elimination equations. To this end, this research aims to use two recently developed intelligent optimization algorithms, dandelion optimizer and gold rush optimizer, to solve this problem. To evaluate the effectiveness of the proposed algorithms, an eleven-level cascaded H-bridge MLI (CHB-MLI) was considered in the study. Simulation results for different modulation indices were obtained, and the accuracy and solution quality were compared with genetic algorithm and particle swarm optimization algorithms. MATLAB/Simulink-based models were used to verify numerical computations, ensuring the reliability of the findings. This research contributes to the field by providing insights into obtaining optimal switching angles and minimizing THD in MLIs by applying intelligent optimization algorithms.

Improved Dujiangyan Irrigation System Optimization (IDISO): A Novel Metaheuristic Algorithm for Hydrochar Characteristics

“…And, because the optimization process of metaheuristic algorithms does not depend on gradient information [20], they find widespread applications in optimization problems for finding the best parameters. For example, the Dujiangyan irrigation system optimization (DISO) [21] is used to construct a DISO-SVM model [21] to detect the impact of dam displacement on dam operation; Particle Swarm Optimization (PSO) [22] is used to construct PSO-NN [14] and PSO-RF [23] models to predict hydrochar properties; the Grey Wolf Optimizer (GWO) [24] is used to construct a GWO-ELM model [25] for monitoring power quality; the Dandelion Optimizer (DO) [26] is used to improve the efficiency of multilevel inverters [27]; the Jellyfish Search Algorithm (JS) [28] is used to discover unknown parameters in fuel cells [29]; Young's Double-Slit Experiment (YDSE) optimizer [30] is used to construct a YDSE-PWM model for predicting dissolved oxygen levels [31]; the Starling Murmuration Optimizer (SMO) [32] algorithm is used to construct an ADA-SMO model [33] for predicting the strength of the mechanical properties of concrete. The "No Free Lunch" theorem states that specific optimization algorithms are suited only to certain optimization problems [34].…”