


Volume 20 No 20 (2022)
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NEURAL NETWORK CONTROLLER BASED QUASI-Z-SOURCE CASCADED INVERTER FOR PHOTOVOLTAIC APPLICATIONS
Thirukovela Nikhith1 , D. Ramesh2 , Dr. T. Anil Kumar
Abstract
In this paper, a Quasi-Z-Source cascaded inverter is employed, and the major goal of this research is
to develop a Neural Network (NN) controller for this inverter. The power converter has three QZS
networks and a cascaded multilevel inverter. Compared to other multilevel inverter topologies, such
as cascaded H-Bridge inverters and NPC inverters, the proposed topology is easier to implement and
requires fewer parts. The proposed inverter and modulation strategy (LSPWM) reduces voltage stress
on the switching components while increasing the boost factor by 50% in comparison to alternative
topologies. The proposed converter's ability to regulate the voltage at the output in grid connected
mode is shown by adjusting the shoot-through duty ratio of the QZS networks. Additionally, it is
demonstrated that the proposed converter with neural network control strategy can interface with the
grid, achieving PSO-MPPT (Particle Swarm Optimization Maximum Power Point Tracking) and UPF
operation consistent with the grid's goals. Simulation findings validate the proposed power circuit
design and the accompanying control method for both static and dynamic operation.
Keywords
Photovoltaic (PV), PSO-MPPT, Neural Network (NN), Cascaded H-Bridge inverter, Quasi-Z-Source (QZS)
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