Loading…

The MPPT control of PV system by using neural networks based on Newton Raphson method

The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT technique...

Full description

Saved in:
Bibliographic Details
Main Authors: Khaldi, Naoufel, Mahmoudi, Hassan, Zazi, Malika, Barradi, Youssef
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 24
container_issue
container_start_page 19
container_title
container_volume
creator Khaldi, Naoufel
Mahmoudi, Hassan
Zazi, Malika
Barradi, Youssef
description The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT techniques. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. The simulations have been carried out on MATLAB/SIMULINK platform for solar photovoltaic system connected to boost dc-dc converter. For three MPPT algorithms, Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper. These results show that the objective is achieved and the MPPT controller based on Back Propagation (BP) neural networks play an effective role to improve the efficiency and reduce the oscillations of PV power system comparing with others control strategies.
doi_str_mv 10.1109/IRSEC.2014.7059894
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_7059894</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7059894</ieee_id><sourcerecordid>7059894</sourcerecordid><originalsourceid>FETCH-LOGICAL-i245t-f1267e9ea0204bc94364f00b47ad574e8083993ea866df5879989f733f30889c3</originalsourceid><addsrcrecordid>eNot0N1KwzAYxvEICs65G9CT3EDnmyZtkkMpUwdTy-zEs5G2b2y1H6PJGL17C-7od_bA_yHkjsGSMdAP6-3HKlmGwMRSQqSVFhfkhgmpteQ8_roks5ArCCRX0TVZOPcDAJwpFTM-I7usQvqaphkt-s4PfUN7S9NP6kbnsaX5SI-u7r5ph8fBNBP-1A-_jubGYUn7jr7hyU9szaFyky36qi9vyZU1jcPF2TnZPa2y5CXYvD-vk8dNUIci8oFlYSxRo4EQRF5owWNhAXIhTRlJgQoU15qjUXFc2khNSUrbqcpyUEoXfE7u_3drRNwfhro1w7g_v8D_AAHXUCA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>The MPPT control of PV system by using neural networks based on Newton Raphson method</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Khaldi, Naoufel ; Mahmoudi, Hassan ; Zazi, Malika ; Barradi, Youssef</creator><creatorcontrib>Khaldi, Naoufel ; Mahmoudi, Hassan ; Zazi, Malika ; Barradi, Youssef</creatorcontrib><description>The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT techniques. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. The simulations have been carried out on MATLAB/SIMULINK platform for solar photovoltaic system connected to boost dc-dc converter. For three MPPT algorithms, Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper. These results show that the objective is achieved and the MPPT controller based on Back Propagation (BP) neural networks play an effective role to improve the efficiency and reduce the oscillations of PV power system comparing with others control strategies.</description><identifier>ISSN: 2380-7385</identifier><identifier>EISBN: 147997336X</identifier><identifier>EISBN: 9781479973361</identifier><identifier>EISBN: 1479973351</identifier><identifier>EISBN: 9781479973354</identifier><identifier>DOI: 10.1109/IRSEC.2014.7059894</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificiel neural networks ; Backpropagation ; Erbium ; incremental conductance ; Load modeling ; MATLAB ; MPPT ; Newton Raphson ; Perturb and observe ; Photovoltaic systems ; Robustness ; Stability analysis</subject><ispartof>2014 International Renewable and Sustainable Energy Conference (IRSEC), 2014, p.19-24</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7059894$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7059894$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Khaldi, Naoufel</creatorcontrib><creatorcontrib>Mahmoudi, Hassan</creatorcontrib><creatorcontrib>Zazi, Malika</creatorcontrib><creatorcontrib>Barradi, Youssef</creatorcontrib><title>The MPPT control of PV system by using neural networks based on Newton Raphson method</title><title>2014 International Renewable and Sustainable Energy Conference (IRSEC)</title><addtitle>IRSEC</addtitle><description>The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT techniques. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. The simulations have been carried out on MATLAB/SIMULINK platform for solar photovoltaic system connected to boost dc-dc converter. For three MPPT algorithms, Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper. These results show that the objective is achieved and the MPPT controller based on Back Propagation (BP) neural networks play an effective role to improve the efficiency and reduce the oscillations of PV power system comparing with others control strategies.</description><subject>Artificiel neural networks</subject><subject>Backpropagation</subject><subject>Erbium</subject><subject>incremental conductance</subject><subject>Load modeling</subject><subject>MATLAB</subject><subject>MPPT</subject><subject>Newton Raphson</subject><subject>Perturb and observe</subject><subject>Photovoltaic systems</subject><subject>Robustness</subject><subject>Stability analysis</subject><issn>2380-7385</issn><isbn>147997336X</isbn><isbn>9781479973361</isbn><isbn>1479973351</isbn><isbn>9781479973354</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNot0N1KwzAYxvEICs65G9CT3EDnmyZtkkMpUwdTy-zEs5G2b2y1H6PJGL17C-7od_bA_yHkjsGSMdAP6-3HKlmGwMRSQqSVFhfkhgmpteQ8_roks5ArCCRX0TVZOPcDAJwpFTM-I7usQvqaphkt-s4PfUN7S9NP6kbnsaX5SI-u7r5ph8fBNBP-1A-_jubGYUn7jr7hyU9szaFyky36qi9vyZU1jcPF2TnZPa2y5CXYvD-vk8dNUIci8oFlYSxRo4EQRF5owWNhAXIhTRlJgQoU15qjUXFc2khNSUrbqcpyUEoXfE7u_3drRNwfhro1w7g_v8D_AAHXUCA</recordid><startdate>201410</startdate><enddate>201410</enddate><creator>Khaldi, Naoufel</creator><creator>Mahmoudi, Hassan</creator><creator>Zazi, Malika</creator><creator>Barradi, Youssef</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201410</creationdate><title>The MPPT control of PV system by using neural networks based on Newton Raphson method</title><author>Khaldi, Naoufel ; Mahmoudi, Hassan ; Zazi, Malika ; Barradi, Youssef</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i245t-f1267e9ea0204bc94364f00b47ad574e8083993ea866df5879989f733f30889c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Artificiel neural networks</topic><topic>Backpropagation</topic><topic>Erbium</topic><topic>incremental conductance</topic><topic>Load modeling</topic><topic>MATLAB</topic><topic>MPPT</topic><topic>Newton Raphson</topic><topic>Perturb and observe</topic><topic>Photovoltaic systems</topic><topic>Robustness</topic><topic>Stability analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Khaldi, Naoufel</creatorcontrib><creatorcontrib>Mahmoudi, Hassan</creatorcontrib><creatorcontrib>Zazi, Malika</creatorcontrib><creatorcontrib>Barradi, Youssef</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Khaldi, Naoufel</au><au>Mahmoudi, Hassan</au><au>Zazi, Malika</au><au>Barradi, Youssef</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The MPPT control of PV system by using neural networks based on Newton Raphson method</atitle><btitle>2014 International Renewable and Sustainable Energy Conference (IRSEC)</btitle><stitle>IRSEC</stitle><date>2014-10</date><risdate>2014</risdate><spage>19</spage><epage>24</epage><pages>19-24</pages><issn>2380-7385</issn><eisbn>147997336X</eisbn><eisbn>9781479973361</eisbn><eisbn>1479973351</eisbn><eisbn>9781479973354</eisbn><abstract>The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Present work deals a comparative analysis of perturb and observe (PO), incremental conductance (IC) and neural network based MPPT techniques. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. The simulations have been carried out on MATLAB/SIMULINK platform for solar photovoltaic system connected to boost dc-dc converter. For three MPPT algorithms, Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper. These results show that the objective is achieved and the MPPT controller based on Back Propagation (BP) neural networks play an effective role to improve the efficiency and reduce the oscillations of PV power system comparing with others control strategies.</abstract><pub>IEEE</pub><doi>10.1109/IRSEC.2014.7059894</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2380-7385
ispartof 2014 International Renewable and Sustainable Energy Conference (IRSEC), 2014, p.19-24
issn 2380-7385
language eng
recordid cdi_ieee_primary_7059894
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificiel neural networks
Backpropagation
Erbium
incremental conductance
Load modeling
MATLAB
MPPT
Newton Raphson
Perturb and observe
Photovoltaic systems
Robustness
Stability analysis
title The MPPT control of PV system by using neural networks based on Newton Raphson method
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T20%3A27%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=The%20MPPT%20control%20of%20PV%20system%20by%20using%20neural%20networks%20based%20on%20Newton%20Raphson%20method&rft.btitle=2014%20International%20Renewable%20and%20Sustainable%20Energy%20Conference%20(IRSEC)&rft.au=Khaldi,%20Naoufel&rft.date=2014-10&rft.spage=19&rft.epage=24&rft.pages=19-24&rft.issn=2380-7385&rft_id=info:doi/10.1109/IRSEC.2014.7059894&rft.eisbn=147997336X&rft.eisbn_list=9781479973361&rft.eisbn_list=1479973351&rft.eisbn_list=9781479973354&rft_dat=%3Cieee_6IE%3E7059894%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i245t-f1267e9ea0204bc94364f00b47ad574e8083993ea866df5879989f733f30889c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7059894&rfr_iscdi=true