Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques

Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one...

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Main Authors: N. Kalaiarasi, A. Sivapriya, Pradeep Vishnuram, Mukesh Pushkarna, Mohit Bajaj, Hossam Kotb, Sadam Alphonse
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:International Transactions on Electrical Energy Systems
Online Access:http://dx.doi.org/10.1155/2023/1134633
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author N. Kalaiarasi
A. Sivapriya
Pradeep Vishnuram
Mukesh Pushkarna
Mohit Bajaj
Hossam Kotb
Sadam Alphonse
author_facet N. Kalaiarasi
A. Sivapriya
Pradeep Vishnuram
Mukesh Pushkarna
Mohit Bajaj
Hossam Kotb
Sadam Alphonse
author_sort N. Kalaiarasi
collection DOAJ
description Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system’s performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted.
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institution Kabale University
issn 2050-7038
language English
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series International Transactions on Electrical Energy Systems
spelling doaj-art-18ab1c8ff30648708e44dab7fad21fad2025-02-03T06:45:16ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/1134633Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT TechniquesN. Kalaiarasi0A. Sivapriya1Pradeep Vishnuram2Mukesh Pushkarna3Mohit Bajaj4Hossam Kotb5Sadam Alphonse6Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical Power and MachinesUFD PAIRecent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system’s performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted.http://dx.doi.org/10.1155/2023/1134633
spellingShingle N. Kalaiarasi
A. Sivapriya
Pradeep Vishnuram
Mukesh Pushkarna
Mohit Bajaj
Hossam Kotb
Sadam Alphonse
Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
International Transactions on Electrical Energy Systems
title Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
title_full Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
title_fullStr Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
title_full_unstemmed Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
title_short Performance Evaluation of Various Z-Source Inverter Topologies for PV Applications Using AI-Based MPPT Techniques
title_sort performance evaluation of various z source inverter topologies for pv applications using ai based mppt techniques
url http://dx.doi.org/10.1155/2023/1134633
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