Design of a 15-Level Non-Modular Multilevel-Inverter in a Grid-Connected Solar PV System: A Hybrid ZOA-SNN Technique

Abstract

In this paper, a hybrid strategy is proposed for an innovative 15-level inverter method for the grid-connected photovoltaic (PV) system. The proposed technique is combined with the Zebra Optimization Algorithm (ZOA) plus the spiking neural network (SNN) and is called the ZOASNN method. The major goals of the ZOASNN approach are to fulfill the power demand of load, lessen harmonics, and improve the PV system power regulation or maximal energy conversion. The multilevel inverter (MLI) is used to operate at symmetrical and asymmetrical configurations aimed at utilizing reduced power components. The proposed ZOASNN controller develops the operating modes of two-generation methods to determine the converter switching states for this purpose. Using this control method, load demands are optimally fulfilled while external disturbances and fluctuations in system parameters are minimized. The proposed strategy is implemented in MATLAB and its performance is estimated with other algorithms, such as the Grasshopper Optimization Algorithm (GOA), the Random Forest Algorithm (RFA), and the Cuckoo Search Algorithm (CSA). The proposed method shows high efficiency, low total harmonics distortion (THD), and lower cost than other existing methods.