Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine
This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed a...
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| Veröffentlicht in: | Journal of engineering for gas turbines and power 2014-06, Vol.136 (6), p.np-np |
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| container_title | Journal of engineering for gas turbines and power |
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| creator | Asgari, Hamid Venturini, Mauro Chen, XiaoQi Sainudiin, Raazesh |
| description | This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation. |
| doi_str_mv | 10.1115/1.4026215 |
| format | Article |
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The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation.</description><identifier>ISSN: 0742-4795</identifier><identifier>EISSN: 1528-8919</identifier><identifier>DOI: 10.1115/1.4026215</identifier><identifier>CODEN: JETPEZ</identifier><language>eng</language><publisher>New York, Ny: ASME</publisher><subject>Applied sciences ; Computer simulation ; Electric power generation ; Electric power plants ; Energy ; Energy. Thermal use of fuels ; Engines and turbines ; Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc ; Exact sciences and technology ; Gas turbines ; Gas Turbines: Controls, Diagnostics, and Instrumentation ; Mathematical models ; Matlab ; Neural networks</subject><ispartof>Journal of engineering for gas turbines and power, 2014-06, Vol.136 (6), p.np-np</ispartof><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a312t-287b03fa94965ac682b8cf41695e9591ab28e8b33aad32b136a617443280014f3</citedby><cites>FETCH-LOGICAL-a312t-287b03fa94965ac682b8cf41695e9591ab28e8b33aad32b136a617443280014f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902,38497</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28494999$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Asgari, Hamid</creatorcontrib><creatorcontrib>Venturini, Mauro</creatorcontrib><creatorcontrib>Chen, XiaoQi</creatorcontrib><creatorcontrib>Sainudiin, Raazesh</creatorcontrib><title>Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine</title><title>Journal of engineering for gas turbines and power</title><addtitle>J. Eng. Gas Turbines Power</addtitle><description>This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation.</description><subject>Applied sciences</subject><subject>Computer simulation</subject><subject>Electric power generation</subject><subject>Electric power plants</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>Engines and turbines</subject><subject>Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc</subject><subject>Exact sciences and technology</subject><subject>Gas turbines</subject><subject>Gas Turbines: Controls, Diagnostics, and Instrumentation</subject><subject>Mathematical models</subject><subject>Matlab</subject><subject>Neural networks</subject><issn>0742-4795</issn><issn>1528-8919</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNo9kD1PwzAQhi0EEuVjYGbxggRDIGc7iT1CxUelIipRFhbrkjrgKrWLnYD497hqxXTDPffq3oeQM8ivAaC4gWuRs5JBsUdGUDCZSQVqn4zySrBMVKo4JEcxLvMcOBfViLw_-4XprPug6Bb01a6GDnvrHfUt7T8NnQd00RrX0zvzid_Wh80GHZ24xRD7YLGjM_9jAp11mKhHjHQ-hNo6c0IOWuyiOd3NY_L2cD8fP2XTl8fJ-HaaIQfWZ0xWdc5bVEKVBTalZLVsWgGlKowqFGDNpJE154gLzmrgJZZQCcGZTC1Ey4_J5TZ3HfzXYGKvVzY2pkv_GD9EnTTkKVyCSOjVFm2CjzGYVq-DXWH41ZDrjT8NeucvsRe7WIwNdm0S0dj4f8CkSKFKJe58y2FcGb30Q3CpreYVL0vB_wCdSHZt</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Asgari, Hamid</creator><creator>Venturini, Mauro</creator><creator>Chen, XiaoQi</creator><creator>Sainudiin, Raazesh</creator><general>ASME</general><general>American Society of Mechanical Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20140601</creationdate><title>Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine</title><author>Asgari, Hamid ; Venturini, Mauro ; Chen, XiaoQi ; Sainudiin, Raazesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a312t-287b03fa94965ac682b8cf41695e9591ab28e8b33aad32b136a617443280014f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Computer simulation</topic><topic>Electric power generation</topic><topic>Electric power plants</topic><topic>Energy</topic><topic>Energy. Thermal use of fuels</topic><topic>Engines and turbines</topic><topic>Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc</topic><topic>Exact sciences and technology</topic><topic>Gas turbines</topic><topic>Gas Turbines: Controls, Diagnostics, and Instrumentation</topic><topic>Mathematical models</topic><topic>Matlab</topic><topic>Neural networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Asgari, Hamid</creatorcontrib><creatorcontrib>Venturini, Mauro</creatorcontrib><creatorcontrib>Chen, XiaoQi</creatorcontrib><creatorcontrib>Sainudiin, Raazesh</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of engineering for gas turbines and power</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Asgari, Hamid</au><au>Venturini, Mauro</au><au>Chen, XiaoQi</au><au>Sainudiin, Raazesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine</atitle><jtitle>Journal of engineering for gas turbines and power</jtitle><stitle>J. Eng. Gas Turbines Power</stitle><date>2014-06-01</date><risdate>2014</risdate><volume>136</volume><issue>6</issue><spage>np</spage><epage>np</epage><pages>np-np</pages><issn>0742-4795</issn><eissn>1528-8919</eissn><coden>JETPEZ</coden><abstract>This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation.</abstract><cop>New York, Ny</cop><pub>ASME</pub><doi>10.1115/1.4026215</doi></addata></record> |
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| ispartof | Journal of engineering for gas turbines and power, 2014-06, Vol.136 (6), p.np-np |
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| language | eng |
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| source | Alma/SFX Local Collection; ASME Transactions Journals (Current) |
| subjects | Applied sciences Computer simulation Electric power generation Electric power plants Energy Energy. Thermal use of fuels Engines and turbines Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc Exact sciences and technology Gas turbines Gas Turbines: Controls, Diagnostics, and Instrumentation Mathematical models Matlab Neural networks |
| title | Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine |
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