A RERNN-SGO Technique for Improved Quasi-Z-Source Cascaded Multilevel Inverter Topology for Interfacing Three Phase Grid-Tie Photovoltaic System

Author:

Bhavani M.1,Manoharan P. S.2

Affiliation:

1. Department of Electrical and Electronics Engineering, Government College of Engineering, Srirangam, Trichy, Tamil Nadu, India

2. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

Abstract

In this manuscript, a proficient control strategy-based improved Quasi-Z-Source Cascaded Multilevel Inverter (QZS-CMI) topology for interfacing photovoltaic (PV) system is proposed. The control strategy is joint execution of both recalling-enhanced recurrent neural network (RERNN) and Shell Game Optimization (SGO), therefore it is called RERNN-SGO technique. The major intention of proposed system determined the photovoltaic system efficiency and maximize the power extraction. The interface among load and PV dc source is to accomplish by the QZSI. At first, the objective function is determined depending on the control constraint and parameters, like current, voltage, modulation index, power. These parameters are utilized to propose RERNN-SGO method. The RERNN-SGO method is used to improve the power delivery, voltage profile and minimum power oscillation for power distribution with load. The maximal power delivered the load is to ensure the artificial intelligence (AI) strategy with the help of maximal power point tracking (MPPT). To regulate shoot through duty ratio and modulation load, the proposed AI method is used. The proposed system is inspired on MATLAB/Simulink site, and then efficiency is related with existing systems under various load conditions. The efficiency of proposed system in Case 1 and Case 2 is 87.363% and 85.3904%.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,General Medicine

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