A Novel Nonisolated Quasi Z-Source Multilevel Inverter for Solar Photovoltaic Energy System Using Robust Technique: An ICSA–RPNN Technique

Abstract

An efficient method primarily depending on nonisolated quasi Z-source (QZS) novel multilevel inverter (NIQZS-NMLI) topology to interconnect photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is the combination of improved chameleon swarm algorithm (ICSA) and recurrent perceptron neural network (RPNN), hence it is named as ICSA–RPNN. Usually, the interface among the PV DC supply, the load is achieved through an NIQZS-NMLI topology. Here, the NIQZS-NMLI topology modeling design is increased with the new storage device to supply the largest amount of power from the PV power production system. The number of switches and full harmonic distortion of the machine are reduced through the NMLI topology, and it is used to achieve better upgrading capability, and reduce the voltage pressure on the entire active switching device has a better modulation index. At first, the objective function is described primarily depending on its controller parameters and control voltages, current, power, and modulation code. These parameters are useful to the inputs of the proposed ICSA-RPNN approach. The ICSA-RPNN methodology enhanced the voltage profile, reducing power distribution, power oscillations simultaneously distributing power to the load. The modulation load is controlled by proposed artificial intelligence (AI) mainly depends on the NIQZS-NMLI topography. In addition, the ICSA-RPNN methodology reduces the injected power, controls the voltage, current and frequency conditions of the DC hyperlink. The ICSA-RPNN approach is performed at MATLAB/Simulink site and the general performance of the output is compared to the existing systems for different load conditions.