N. Kalaiarasi1, Subhranshu Sekhar Dash2, S. Paramasivam3, and C. Bharatiraja This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology ,SRM Nagar, Kattankulathur, 603203, Kanchipuram, Chennai, TN, India
2Government College of Engineering, Keonjhar, Odisha, India
3Danfoss Industries Ltd, Chennai, Tamilnadu, India


Received: August 1, 2020
Accepted: October 22, 2020
Publication Date: April 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202104_24(2).0015  


Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

Keywords: Shoot through duty cycle, MPPT, Fuzzy Logic Controller, Artificial Neural Network, ANFIS, Z-Source inverter


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