Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization
This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excit...
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Veröffentlicht in: | IEEE transactions on energy conversion 2023-06, Vol.38 (2), p.1-10 |
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description | This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models. |
doi_str_mv | 10.1109/TEC.2022.3221215 |
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The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.</description><identifier>ISSN: 0885-8969</identifier><identifier>EISSN: 1558-0059</identifier><identifier>DOI: 10.1109/TEC.2022.3221215</identifier><identifier>CODEN: ITCNE4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Controllers ; Core loss ; Electric converters ; Energy conversion ; Field-oriented control ; Fuzzy logic ; Generators ; hedge algebra ; induction generator ; Induction generators ; Iron ; Magnetic flux ; Magnetic saturation ; Mathematical models ; model-based optimization ; MPPT ; Optimization ; Proportional integral ; Rotors ; Saturation magnetization ; Speed control ; Stators ; Wind power ; wind turbine ; Wind turbines</subject><ispartof>IEEE transactions on energy conversion, 2023-06, Vol.38 (2), p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-8627793ab6fb9d446cba89b40f5c2f37895a686dc29482acef1a301888c6923e3</citedby><cites>FETCH-LOGICAL-c291t-8627793ab6fb9d446cba89b40f5c2f37895a686dc29482acef1a301888c6923e3</cites><orcidid>0000-0001-5207-9070 ; 0000-0001-7796-4551 ; 0000-0001-8680-0524 ; 0000-0003-3987-3792</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9944886$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9944886$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bubalo, Matija</creatorcontrib><creatorcontrib>Basic, Mateo</creatorcontrib><creatorcontrib>Vukadinovic, Dinko</creatorcontrib><creatorcontrib>Grgic, Ivan</creatorcontrib><title>Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization</title><title>IEEE transactions on energy conversion</title><addtitle>TEC</addtitle><description>This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.</description><subject>Controllers</subject><subject>Core loss</subject><subject>Electric converters</subject><subject>Energy conversion</subject><subject>Field-oriented control</subject><subject>Fuzzy logic</subject><subject>Generators</subject><subject>hedge algebra</subject><subject>induction generator</subject><subject>Induction generators</subject><subject>Iron</subject><subject>Magnetic flux</subject><subject>Magnetic saturation</subject><subject>Mathematical models</subject><subject>model-based optimization</subject><subject>MPPT</subject><subject>Optimization</subject><subject>Proportional integral</subject><subject>Rotors</subject><subject>Saturation magnetization</subject><subject>Speed control</subject><subject>Stators</subject><subject>Wind power</subject><subject>wind turbine</subject><subject>Wind turbines</subject><issn>0885-8969</issn><issn>1558-0059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKt3wUvA89Z8bLKTY13qB7R4aIsXIWR3syWlzdZkW6i_3pSKlxkYnndmeBC6p2REKVFPi0k5YoSxEWeMMiou0IAKARkhQl2iAQEQGSiprtFNjGtCaC4YHaCvT-cbPPE2rI647PzBhug6j-fH2NstXkbnV3jcHIyvbYPnO5tqwvrQbbBJyVnX2E32bGKaT7sY8cx5t3U_pk9bbtFVazbR3v31IVq-TBblWzb9eH0vx9OsZor2GUhWFIqbSraVavJc1pUBVeWkFTVreQFKGAmySXQOzNS2pYYTCgC1VIxbPkSP57270H3vbez1utsHn05qBhR4QbmgiSJnqg7p0WBbvQtua8JRU6JPDnVyqE8O9Z_DFHk4R5y19h9XKs8BJP8Fd7hsbA</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Bubalo, Matija</creator><creator>Basic, Mateo</creator><creator>Vukadinovic, Dinko</creator><creator>Grgic, Ivan</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>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5207-9070</orcidid><orcidid>https://orcid.org/0000-0001-7796-4551</orcidid><orcidid>https://orcid.org/0000-0001-8680-0524</orcidid><orcidid>https://orcid.org/0000-0003-3987-3792</orcidid></search><sort><creationdate>20230601</creationdate><title>Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization</title><author>Bubalo, Matija ; Basic, Mateo ; Vukadinovic, Dinko ; Grgic, Ivan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-8627793ab6fb9d446cba89b40f5c2f37895a686dc29482acef1a301888c6923e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Controllers</topic><topic>Core loss</topic><topic>Electric converters</topic><topic>Energy conversion</topic><topic>Field-oriented control</topic><topic>Fuzzy logic</topic><topic>Generators</topic><topic>hedge algebra</topic><topic>induction generator</topic><topic>Induction generators</topic><topic>Iron</topic><topic>Magnetic flux</topic><topic>Magnetic saturation</topic><topic>Mathematical models</topic><topic>model-based optimization</topic><topic>MPPT</topic><topic>Optimization</topic><topic>Proportional integral</topic><topic>Rotors</topic><topic>Saturation magnetization</topic><topic>Speed control</topic><topic>Stators</topic><topic>Wind power</topic><topic>wind turbine</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bubalo, Matija</creatorcontrib><creatorcontrib>Basic, Mateo</creatorcontrib><creatorcontrib>Vukadinovic, Dinko</creatorcontrib><creatorcontrib>Grgic, Ivan</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 (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on energy conversion</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bubalo, Matija</au><au>Basic, Mateo</au><au>Vukadinovic, Dinko</au><au>Grgic, Ivan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization</atitle><jtitle>IEEE transactions on energy conversion</jtitle><stitle>TEC</stitle><date>2023-06-01</date><risdate>2023</risdate><volume>38</volume><issue>2</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>0885-8969</issn><eissn>1558-0059</eissn><coden>ITCNE4</coden><abstract>This paper presents a new optimization strategy for a stand-alone wind energy conversion system (WECS). The WECS is comprised of a variable-speed wind turbine (WT) with a vector-controlled self-excited induction generator (SEIG), a three-phase full-bridge converter, and a DC-bus containing the excitation capacitor, batteries, and a load. The control strategy incorporates an advanced model-based SEIG loss minimization and fuzzy-logic WT optimization. The latter utilizes a hedge-algebra speed controller to ensure fast response with practically no overshoot in the whole WT operating range, which cannot be achieved with the conventional proportional-integral (PI) controller. Consequently, the WT optimization time step is shortened and its convergence accelerated. The proposed SEIG loss minimization is based on the corresponding mathematical model that accounts for magnetic saturation and variable stray load and iron losses. Simultaneous optimization of the WT and SEIG is enabled, which results in greater total energy output compared to the successive WT-SEIG optimization. The proposed control strategy is run in real-time using the DS1103 board (dSpace) with a 1.5 kW SEIG driven by an emulated WT. It is experimentally evaluated over a wide WT operating range and compared with several competing strategies involving successive optimization, PI speed control and/or less elaborate SEIG models.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TEC.2022.3221215</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5207-9070</orcidid><orcidid>https://orcid.org/0000-0001-7796-4551</orcidid><orcidid>https://orcid.org/0000-0001-8680-0524</orcidid><orcidid>https://orcid.org/0000-0003-3987-3792</orcidid></addata></record> |
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subjects | Controllers Core loss Electric converters Energy conversion Field-oriented control Fuzzy logic Generators hedge algebra induction generator Induction generators Iron Magnetic flux Magnetic saturation Mathematical models model-based optimization MPPT Optimization Proportional integral Rotors Saturation magnetization Speed control Stators Wind power wind turbine Wind turbines |
title | Wind Energy Conversion System Using Advanced Speed Control and Model-Based Loss Minimization |
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