Loading…
A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions
In this article, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking t...
Saved in:
Published in: | IEEE transactions on sustainable energy 2024-01, Vol.15 (1), p.328-338 |
---|---|
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-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3 |
---|---|
cites | cdi_FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3 |
container_end_page | 338 |
container_issue | 1 |
container_start_page | 328 |
container_title | IEEE transactions on sustainable energy |
container_volume | 15 |
creator | Ye, Song-Pei Liu, Yi-Hua Pai, Hung-Yu Sangwongwanich, Ariya Blaabjerg, Frede |
description | In this article, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values. |
doi_str_mv | 10.1109/TSTE.2023.3284866 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2904589329</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10148801</ieee_id><sourcerecordid>2904589329</sourcerecordid><originalsourceid>FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3</originalsourceid><addsrcrecordid>eNpNkE1rAjEQhkNpoWL9AYUeAj2vzdfuJkcr1hbULrh6DTEfdWXd2GRb6r_vilI6lxmG552BB4B7jIYYI_FULsvJkCBCh5RwxrPsCvSwYCKhiObXfzMRt2AQ4w51RSnNKOqB9Qgu_Let4WixSJ5VtAZO50VRwrltt95A5wMs1nB5jK3dR7hqjA1w7PeH2v7AQoW2UjVcbpWpmo9u35iqrXwT78CNU3W0g0vvg9XLpBy_JrP36dt4NEs0EaxNmGKOU0UsTnG-wRhnuWM6s8QxhzOUpRutjDYdZJnAgnMnDE-JZukmNyrVtA8ez3cPwX9-2djKnf8KTfdSEoFYygUloqPwmdLBxxisk4dQ7VU4SozkyaA8GZQng_JisMs8nDOVtfYfjxnnCNNfOMxqwQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2904589329</pqid></control><display><type>article</type><title>A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions</title><source>IEEE Xplore (Online service)</source><creator>Ye, Song-Pei ; Liu, Yi-Hua ; Pai, Hung-Yu ; Sangwongwanich, Ariya ; Blaabjerg, Frede</creator><creatorcontrib>Ye, Song-Pei ; Liu, Yi-Hua ; Pai, Hung-Yu ; Sangwongwanich, Ariya ; Blaabjerg, Frede</creatorcontrib><description>In this article, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.</description><identifier>ISSN: 1949-3029</identifier><identifier>EISSN: 1949-3037</identifier><identifier>DOI: 10.1109/TSTE.2023.3284866</identifier><identifier>CODEN: ITSEAJ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; Artificial neural network (ANN) ; Artificial neural networks ; Computer simulation ; global maximum power point tracking (GMPPT) ; Illuminance ; Maximum power point trackers ; Maximum power tracking ; Neural networks ; Performance evaluation ; Performance indices ; photovoltaic (PV) ; Photovoltaic systems ; Shading ; Solar panels ; Temperature requirements ; Temperature sensors ; Voltage control ; Voltage measurement</subject><ispartof>IEEE transactions on sustainable energy, 2024-01, Vol.15 (1), p.328-338</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3</citedby><cites>FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3</cites><orcidid>0000-0002-2587-0024 ; 0000-0001-7593-0155 ; 0000-0001-8311-7412</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10148801$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Ye, Song-Pei</creatorcontrib><creatorcontrib>Liu, Yi-Hua</creatorcontrib><creatorcontrib>Pai, Hung-Yu</creatorcontrib><creatorcontrib>Sangwongwanich, Ariya</creatorcontrib><creatorcontrib>Blaabjerg, Frede</creatorcontrib><title>A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions</title><title>IEEE transactions on sustainable energy</title><addtitle>TSTE</addtitle><description>In this article, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural network (ANN)</subject><subject>Artificial neural networks</subject><subject>Computer simulation</subject><subject>global maximum power point tracking (GMPPT)</subject><subject>Illuminance</subject><subject>Maximum power point trackers</subject><subject>Maximum power tracking</subject><subject>Neural networks</subject><subject>Performance evaluation</subject><subject>Performance indices</subject><subject>photovoltaic (PV)</subject><subject>Photovoltaic systems</subject><subject>Shading</subject><subject>Solar panels</subject><subject>Temperature requirements</subject><subject>Temperature sensors</subject><subject>Voltage control</subject><subject>Voltage measurement</subject><issn>1949-3029</issn><issn>1949-3037</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNkE1rAjEQhkNpoWL9AYUeAj2vzdfuJkcr1hbULrh6DTEfdWXd2GRb6r_vilI6lxmG552BB4B7jIYYI_FULsvJkCBCh5RwxrPsCvSwYCKhiObXfzMRt2AQ4w51RSnNKOqB9Qgu_Let4WixSJ5VtAZO50VRwrltt95A5wMs1nB5jK3dR7hqjA1w7PeH2v7AQoW2UjVcbpWpmo9u35iqrXwT78CNU3W0g0vvg9XLpBy_JrP36dt4NEs0EaxNmGKOU0UsTnG-wRhnuWM6s8QxhzOUpRutjDYdZJnAgnMnDE-JZukmNyrVtA8ez3cPwX9-2djKnf8KTfdSEoFYygUloqPwmdLBxxisk4dQ7VU4SozkyaA8GZQng_JisMs8nDOVtfYfjxnnCNNfOMxqwQ</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Ye, Song-Pei</creator><creator>Liu, Yi-Hua</creator><creator>Pai, Hung-Yu</creator><creator>Sangwongwanich, Ariya</creator><creator>Blaabjerg, Frede</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-2587-0024</orcidid><orcidid>https://orcid.org/0000-0001-7593-0155</orcidid><orcidid>https://orcid.org/0000-0001-8311-7412</orcidid></search><sort><creationdate>202401</creationdate><title>A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions</title><author>Ye, Song-Pei ; Liu, Yi-Hua ; Pai, Hung-Yu ; Sangwongwanich, Ariya ; Blaabjerg, Frede</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial neural network (ANN)</topic><topic>Artificial neural networks</topic><topic>Computer simulation</topic><topic>global maximum power point tracking (GMPPT)</topic><topic>Illuminance</topic><topic>Maximum power point trackers</topic><topic>Maximum power tracking</topic><topic>Neural networks</topic><topic>Performance evaluation</topic><topic>Performance indices</topic><topic>photovoltaic (PV)</topic><topic>Photovoltaic systems</topic><topic>Shading</topic><topic>Solar panels</topic><topic>Temperature requirements</topic><topic>Temperature sensors</topic><topic>Voltage control</topic><topic>Voltage measurement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ye, Song-Pei</creatorcontrib><creatorcontrib>Liu, Yi-Hua</creatorcontrib><creatorcontrib>Pai, Hung-Yu</creatorcontrib><creatorcontrib>Sangwongwanich, Ariya</creatorcontrib><creatorcontrib>Blaabjerg, Frede</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library Online</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>IEEE transactions on sustainable energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ye, Song-Pei</au><au>Liu, Yi-Hua</au><au>Pai, Hung-Yu</au><au>Sangwongwanich, Ariya</au><au>Blaabjerg, Frede</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions</atitle><jtitle>IEEE transactions on sustainable energy</jtitle><stitle>TSTE</stitle><date>2024-01</date><risdate>2024</risdate><volume>15</volume><issue>1</issue><spage>328</spage><epage>338</epage><pages>328-338</pages><issn>1949-3029</issn><eissn>1949-3037</eissn><coden>ITSEAJ</coden><abstract>In this article, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TSTE.2023.3284866</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-2587-0024</orcidid><orcidid>https://orcid.org/0000-0001-7593-0155</orcidid><orcidid>https://orcid.org/0000-0001-8311-7412</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1949-3029 |
ispartof | IEEE transactions on sustainable energy, 2024-01, Vol.15 (1), p.328-338 |
issn | 1949-3029 1949-3037 |
language | eng |
recordid | cdi_proquest_journals_2904589329 |
source | IEEE Xplore (Online service) |
subjects | Accuracy Algorithms Artificial neural network (ANN) Artificial neural networks Computer simulation global maximum power point tracking (GMPPT) Illuminance Maximum power point trackers Maximum power tracking Neural networks Performance evaluation Performance indices photovoltaic (PV) Photovoltaic systems Shading Solar panels Temperature requirements Temperature sensors Voltage control Voltage measurement |
title | A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T20%3A23%3A53IST&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=A%20Novel%20ANN-Based%20GMPPT%20Method%20for%20PV%20Systems%20Under%20Complex%20Partial%20Shading%20Conditions&rft.jtitle=IEEE%20transactions%20on%20sustainable%20energy&rft.au=Ye,%20Song-Pei&rft.date=2024-01&rft.volume=15&rft.issue=1&rft.spage=328&rft.epage=338&rft.pages=328-338&rft.issn=1949-3029&rft.eissn=1949-3037&rft.coden=ITSEAJ&rft_id=info:doi/10.1109/TSTE.2023.3284866&rft_dat=%3Cproquest_cross%3E2904589329%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c294t-4a4f83a2e1517b11167f4c6e2f4f16065bcadcd4f8e491988f9d852c45b7da5c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2904589329&rft_id=info:pmid/&rft_ieee_id=10148801&rfr_iscdi=true |