Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant
A large-scale once-through-type ultrasupercritical boiler power plant is investigated for the development of an analyzable model for use in developing an intelligent control system. Using data from the power plant, a model is realized using dynamically recurrent neural networks (NN). This requires t...
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| Veröffentlicht in: | IEEE transactions on energy conversion 2010-12, Vol.25 (4), p.1063-1070 |
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| container_title | IEEE transactions on energy conversion |
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| creator | Lee, Kwang Y Van Sickel, Joel H Hoffman, Jason A Won-Hee Jung Sung-Ho Kim |
| description | A large-scale once-through-type ultrasupercritical boiler power plant is investigated for the development of an analyzable model for use in developing an intelligent control system. Using data from the power plant, a model is realized using dynamically recurrent neural networks (NN). This requires the partitioning of multiple subsystems, which are each represented by an individual NN that when combined form the whole plant model. Modified predictive optimal control was used to drive the plant to desired states; however, due to the computational intensity of this approach, it could not be executed quickly enough to satisfy project requirements. As an alternative, a reference governor was implemented along with a PID feedback control system that utilizes intelligent gain tuning, which, while more complicated, satisfied the computational speed required for the controller to be realized. |
| doi_str_mv | 10.1109/TEC.2010.2060488 |
| format | Article |
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Using data from the power plant, a model is realized using dynamically recurrent neural networks (NN). This requires the partitioning of multiple subsystems, which are each represented by an individual NN that when combined form the whole plant model. Modified predictive optimal control was used to drive the plant to desired states; however, due to the computational intensity of this approach, it could not be executed quickly enough to satisfy project requirements. As an alternative, a reference governor was implemented along with a PID feedback control system that utilizes intelligent gain tuning, which, while more complicated, satisfied the computational speed required for the controller to be realized.</description><identifier>ISSN: 0885-8969</identifier><identifier>EISSN: 1558-0059</identifier><identifier>DOI: 10.1109/TEC.2010.2060488</identifier><identifier>CODEN: ITCNE4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Boilers ; Gain tuning ; Intelligent control ; modified predictive optimal control (MPOC) ; Power generation ; Tuning ; Turbines ; ultrasupercritical (USC) power plant</subject><ispartof>IEEE transactions on energy conversion, 2010-12, Vol.25 (4), p.1063-1070</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c323t-463bb9d932c833ab8b7095990d4994cbdd867bba52a1d3e3e98cf5fb7f6634593</citedby><cites>FETCH-LOGICAL-c323t-463bb9d932c833ab8b7095990d4994cbdd867bba52a1d3e3e98cf5fb7f6634593</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5594627$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27907,27908,54741</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5594627$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lee, Kwang Y</creatorcontrib><creatorcontrib>Van Sickel, Joel H</creatorcontrib><creatorcontrib>Hoffman, Jason A</creatorcontrib><creatorcontrib>Won-Hee Jung</creatorcontrib><creatorcontrib>Sung-Ho Kim</creatorcontrib><title>Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant</title><title>IEEE transactions on energy conversion</title><addtitle>TEC</addtitle><description>A large-scale once-through-type ultrasupercritical boiler power plant is investigated for the development of an analyzable model for use in developing an intelligent control system. Using data from the power plant, a model is realized using dynamically recurrent neural networks (NN). This requires the partitioning of multiple subsystems, which are each represented by an individual NN that when combined form the whole plant model. Modified predictive optimal control was used to drive the plant to desired states; however, due to the computational intensity of this approach, it could not be executed quickly enough to satisfy project requirements. As an alternative, a reference governor was implemented along with a PID feedback control system that utilizes intelligent gain tuning, which, while more complicated, satisfied the computational speed required for the controller to be realized.</description><subject>Artificial neural networks</subject><subject>Boilers</subject><subject>Gain tuning</subject><subject>Intelligent control</subject><subject>modified predictive optimal control (MPOC)</subject><subject>Power generation</subject><subject>Tuning</subject><subject>Turbines</subject><subject>ultrasupercritical (USC) power plant</subject><issn>0885-8969</issn><issn>1558-0059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEQhoMoWKt3wcuCB09bk83HJket9QMKFWy9hmx2tt2y3dRkF_Hfm6XFg5cZZnjeYXgQuiZ4QghW98vZdJLhOGVYYCblCRoRzmWKMVenaISl5KlUQp2jixC2GBPGMzJCn1PXdt41DfjkCUK9bpPK-cQkc-PXkH5Y00CyajpvQr8Hb33d1XGXLFoL6XLjXb_eJI-uHvLv7nuojWm7S3RWmSbA1bGP0ep5tpy-pvPFy9v0YZ5amtEuZYIWhSoVzayk1BSyyLHiSuGSKcVsUZZS5EVheGZISYGCkrbiVZFXQlDGFR2ju8PdvXdfPYRO7-pgoYk_gOuDlkxFF1iQSN7-I7eu9218ThNMMRGE5yxS-EBZ70LwUOm9r3fG_0RID5519KwHz_roOUZuDpEaAP5wzhUTWU5_ASAaeFc</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Lee, Kwang Y</creator><creator>Van Sickel, Joel H</creator><creator>Hoffman, Jason A</creator><creator>Won-Hee Jung</creator><creator>Sung-Ho Kim</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial neural networks Boilers Gain tuning Intelligent control modified predictive optimal control (MPOC) Power generation Tuning Turbines ultrasupercritical (USC) power plant |
| title | Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant |
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