Loading…
Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT
The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscur...
Saved in:
Published in: | Smart grids and sustainable energy 2023-03, Vol.8 (2), p.7, Article 7 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23 |
---|---|
cites | cdi_FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23 |
container_end_page | |
container_issue | 2 |
container_start_page | 7 |
container_title | Smart grids and sustainable energy |
container_volume | 8 |
creator | Kouser, Sanam Dheep, G. Raam Bansal, Ramesh C. |
description | The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscured by clouds or structures, several local maximum power peaks (LMPPs) appear in the solar cell characteristics. Traditional MPPT algorithms, rather than following the global peak power point (GPPP), are preferable to following the local peak power point. If partial shading causes numerous LPPPs, it is necessary to look into how the MPPT technique can keep track of GPPP. Employing soft computing approaches such as the hybrid neural network/fuzzy method with variable step size perturb and observing MPPT, it is possible to trace the GPPP and also augment solar energy extraction. The present research paper focuses on hybrid fuzzy/neural network MPPT integrated with a high-step-up DC-DC converter to harvest the utmost power from the solar PV array. The voltage transients are reduced by controlling the DC link voltage along with solar radiation and temperature variations. The proposed MPPT technique is shown to be effective under both uniform and partial shade conditions in a series of simulations. From the test results, the efficiency of the overall system has increased from 91 to 98% for partial shading and uniform operating conditions. |
doi_str_mv | 10.1007/s40866-023-00161-6 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2890356368</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2890356368</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23</originalsourceid><addsrcrecordid>eNpNkMtOwzAQRSMEElXpD7CyxNrUj8Rxl1D1gdRCpLTdWk7iFJcmKbajNv0D_hpDWbCaGc25M9IJgnuMHjFC8dCGiDMGEaEQIcwwZFdBj8QUQ454fP2vvw0G1u4QQpTQiMW8F3wt5UlXbQWS5qgMmJyckbnTTQ10DRJpnJZ7kL7LQhVgZnQBx01dq9z5MdmAtLNOVWBtdb0F8y7zAJi253MHFs1W58NX1Rqff1Xu2JgP-Cytz22k0TLbK5A6dQCpPiuwTJLVXXBTyr1Vg7_aD9bTyWo8h4u32cv4aQFzyqiDEQvZiIxKzGXOJGYoC0lMCqYQY0ThLJYlliqXcVhGGafKr3hW5BEukCQsI7QfPFzuHkzz2SrrxK5pTe1fCsJHyHuhjHuKXKjcNNYaVYqD0ZU0ncBI_FgXF-vCWxe_1gWj3yCsdUY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2890356368</pqid></control><display><type>article</type><title>Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT</title><source>ABI/INFORM Global</source><source>Springer Nature</source><creator>Kouser, Sanam ; Dheep, G. Raam ; Bansal, Ramesh C.</creator><creatorcontrib>Kouser, Sanam ; Dheep, G. Raam ; Bansal, Ramesh C.</creatorcontrib><description>The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscured by clouds or structures, several local maximum power peaks (LMPPs) appear in the solar cell characteristics. Traditional MPPT algorithms, rather than following the global peak power point (GPPP), are preferable to following the local peak power point. If partial shading causes numerous LPPPs, it is necessary to look into how the MPPT technique can keep track of GPPP. Employing soft computing approaches such as the hybrid neural network/fuzzy method with variable step size perturb and observing MPPT, it is possible to trace the GPPP and also augment solar energy extraction. The present research paper focuses on hybrid fuzzy/neural network MPPT integrated with a high-step-up DC-DC converter to harvest the utmost power from the solar PV array. The voltage transients are reduced by controlling the DC link voltage along with solar radiation and temperature variations. The proposed MPPT technique is shown to be effective under both uniform and partial shade conditions in a series of simulations. From the test results, the efficiency of the overall system has increased from 91 to 98% for partial shading and uniform operating conditions.</description><identifier>ISSN: 2731-8087</identifier><identifier>EISSN: 2731-8087</identifier><identifier>EISSN: 2199-4706</identifier><identifier>DOI: 10.1007/s40866-023-00161-6</identifier><language>eng</language><publisher>Heidelberg: Springer Nature B.V</publisher><subject>Algorithms ; Arrays ; Artificial neural networks ; Electrical surges ; Fuzzy logic ; Hybrid systems ; Maximum power tracking ; Neural networks ; Photovoltaic cells ; Photovoltaics ; Radiation ; Shading ; Soft computing ; Solar cells ; Solar energy ; Solar radiation ; Voltage ; Voltage converters (DC to DC)</subject><ispartof>Smart grids and sustainable energy, 2023-03, Vol.8 (2), p.7, Article 7</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23</citedby><cites>FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23</cites><orcidid>0000-0002-1725-2648</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2890356368?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,27901,27902,36037,44339</link.rule.ids></links><search><creatorcontrib>Kouser, Sanam</creatorcontrib><creatorcontrib>Dheep, G. Raam</creatorcontrib><creatorcontrib>Bansal, Ramesh C.</creatorcontrib><title>Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT</title><title>Smart grids and sustainable energy</title><description>The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscured by clouds or structures, several local maximum power peaks (LMPPs) appear in the solar cell characteristics. Traditional MPPT algorithms, rather than following the global peak power point (GPPP), are preferable to following the local peak power point. If partial shading causes numerous LPPPs, it is necessary to look into how the MPPT technique can keep track of GPPP. Employing soft computing approaches such as the hybrid neural network/fuzzy method with variable step size perturb and observing MPPT, it is possible to trace the GPPP and also augment solar energy extraction. The present research paper focuses on hybrid fuzzy/neural network MPPT integrated with a high-step-up DC-DC converter to harvest the utmost power from the solar PV array. The voltage transients are reduced by controlling the DC link voltage along with solar radiation and temperature variations. The proposed MPPT technique is shown to be effective under both uniform and partial shade conditions in a series of simulations. From the test results, the efficiency of the overall system has increased from 91 to 98% for partial shading and uniform operating conditions.</description><subject>Algorithms</subject><subject>Arrays</subject><subject>Artificial neural networks</subject><subject>Electrical surges</subject><subject>Fuzzy logic</subject><subject>Hybrid systems</subject><subject>Maximum power tracking</subject><subject>Neural networks</subject><subject>Photovoltaic cells</subject><subject>Photovoltaics</subject><subject>Radiation</subject><subject>Shading</subject><subject>Soft computing</subject><subject>Solar cells</subject><subject>Solar energy</subject><subject>Solar radiation</subject><subject>Voltage</subject><subject>Voltage converters (DC to DC)</subject><issn>2731-8087</issn><issn>2731-8087</issn><issn>2199-4706</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNpNkMtOwzAQRSMEElXpD7CyxNrUj8Rxl1D1gdRCpLTdWk7iFJcmKbajNv0D_hpDWbCaGc25M9IJgnuMHjFC8dCGiDMGEaEQIcwwZFdBj8QUQ454fP2vvw0G1u4QQpTQiMW8F3wt5UlXbQWS5qgMmJyckbnTTQ10DRJpnJZ7kL7LQhVgZnQBx01dq9z5MdmAtLNOVWBtdb0F8y7zAJi253MHFs1W58NX1Rqff1Xu2JgP-Cytz22k0TLbK5A6dQCpPiuwTJLVXXBTyr1Vg7_aD9bTyWo8h4u32cv4aQFzyqiDEQvZiIxKzGXOJGYoC0lMCqYQY0ThLJYlliqXcVhGGafKr3hW5BEukCQsI7QfPFzuHkzz2SrrxK5pTe1fCsJHyHuhjHuKXKjcNNYaVYqD0ZU0ncBI_FgXF-vCWxe_1gWj3yCsdUY</recordid><startdate>20230329</startdate><enddate>20230329</enddate><creator>Kouser, Sanam</creator><creator>Dheep, G. Raam</creator><creator>Bansal, Ramesh C.</creator><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>PYYUZ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-1725-2648</orcidid></search><sort><creationdate>20230329</creationdate><title>Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT</title><author>Kouser, Sanam ; Dheep, G. Raam ; Bansal, Ramesh C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Arrays</topic><topic>Artificial neural networks</topic><topic>Electrical surges</topic><topic>Fuzzy logic</topic><topic>Hybrid systems</topic><topic>Maximum power tracking</topic><topic>Neural networks</topic><topic>Photovoltaic cells</topic><topic>Photovoltaics</topic><topic>Radiation</topic><topic>Shading</topic><topic>Soft computing</topic><topic>Solar cells</topic><topic>Solar energy</topic><topic>Solar radiation</topic><topic>Voltage</topic><topic>Voltage converters (DC to DC)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kouser, Sanam</creatorcontrib><creatorcontrib>Dheep, G. Raam</creatorcontrib><creatorcontrib>Bansal, Ramesh C.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>ProQuest Engineering Database</collection><collection>Environmental Science Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Smart grids and sustainable energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kouser, Sanam</au><au>Dheep, G. Raam</au><au>Bansal, Ramesh C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT</atitle><jtitle>Smart grids and sustainable energy</jtitle><date>2023-03-29</date><risdate>2023</risdate><volume>8</volume><issue>2</issue><spage>7</spage><pages>7-</pages><artnum>7</artnum><issn>2731-8087</issn><eissn>2731-8087</eissn><eissn>2199-4706</eissn><abstract>The photovoltaic (PV) system’s output power varies owing to solar radiation’s irregularity, which confines their usage for various applications. Implementation of maximum power tracking (MPT) algorithms increases the efficiency and power generated from solar cells. When the array is partially obscured by clouds or structures, several local maximum power peaks (LMPPs) appear in the solar cell characteristics. Traditional MPPT algorithms, rather than following the global peak power point (GPPP), are preferable to following the local peak power point. If partial shading causes numerous LPPPs, it is necessary to look into how the MPPT technique can keep track of GPPP. Employing soft computing approaches such as the hybrid neural network/fuzzy method with variable step size perturb and observing MPPT, it is possible to trace the GPPP and also augment solar energy extraction. The present research paper focuses on hybrid fuzzy/neural network MPPT integrated with a high-step-up DC-DC converter to harvest the utmost power from the solar PV array. The voltage transients are reduced by controlling the DC link voltage along with solar radiation and temperature variations. The proposed MPPT technique is shown to be effective under both uniform and partial shade conditions in a series of simulations. From the test results, the efficiency of the overall system has increased from 91 to 98% for partial shading and uniform operating conditions.</abstract><cop>Heidelberg</cop><pub>Springer Nature B.V</pub><doi>10.1007/s40866-023-00161-6</doi><orcidid>https://orcid.org/0000-0002-1725-2648</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2731-8087 |
ispartof | Smart grids and sustainable energy, 2023-03, Vol.8 (2), p.7, Article 7 |
issn | 2731-8087 2731-8087 2199-4706 |
language | eng |
recordid | cdi_proquest_journals_2890356368 |
source | ABI/INFORM Global; Springer Nature |
subjects | Algorithms Arrays Artificial neural networks Electrical surges Fuzzy logic Hybrid systems Maximum power tracking Neural networks Photovoltaic cells Photovoltaics Radiation Shading Soft computing Solar cells Solar energy Solar radiation Voltage Voltage converters (DC to DC) |
title | Maximum Power Extraction in Partial Shaded Grid-Connected PV System Using Hybrid Fuzzy Logic/Neural Network-Based Variable Step Size MPPT |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T15%3A27%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Maximum%20Power%20Extraction%20in%20Partial%20Shaded%20Grid-Connected%20PV%20System%20Using%20Hybrid%20Fuzzy%20Logic/Neural%20Network-Based%20Variable%20Step%20Size%20MPPT&rft.jtitle=Smart%20grids%20and%20sustainable%20energy&rft.au=Kouser,%20Sanam&rft.date=2023-03-29&rft.volume=8&rft.issue=2&rft.spage=7&rft.pages=7-&rft.artnum=7&rft.issn=2731-8087&rft.eissn=2731-8087&rft_id=info:doi/10.1007/s40866-023-00161-6&rft_dat=%3Cproquest_cross%3E2890356368%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c363t-5646929f18ac6a160b4272d6e0662e1b7af1aeca74f5b83e2d68bdc51d0a26b23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2890356368&rft_id=info:pmid/&rfr_iscdi=true |