Thermal power prediction of nuclear power plant using neural network and parity space model
A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for input preprocessing and a backpropagation network algorithm for network learning are used for the po...
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| Veröffentlicht in: | IEEE transactions on nuclear science 1991-04, Vol.38 (2), p.866-872 |
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| container_end_page | 872 |
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| container_issue | 2 |
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| container_title | IEEE transactions on nuclear science |
| container_volume | 38 |
| creator | Roh Myung-Sub Cheon Se-Woo Chang Soon-Heung |
| description | A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for input preprocessing and a backpropagation network algorithm for network learning are used for the power prediction system. Case studies were performed with emphasis on the applicability of the network in a steady-state high-power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. They also show that the error signals resulting from instrumentation problems can be properly treated even when the signals comprising various patterns are noisy or incomplete.< > |
| doi_str_mv | 10.1109/23.289402 |
| format | Article |
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Thermal use of fuels ; EQUATIONS ; Exact sciences and technology ; Fission nuclear power plants ; GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE ; GENERAL STUDIES OF NUCLEAR REACTORS ; HEAT RATE ; Installations for energy generation and conversion: thermal and electrical energy ; MATHEMATICAL LOGIC ; NEURAL NETWORKS ; NUCLEAR FACILITIES ; NUCLEAR POWER PLANTS ; OPERATION ; Power generation ; POWER PLANTS ; Power system modeling ; PREDICTION EQUATIONS ; Predictive models ; REACTOR INSTRUMENTATION ; REACTOR OPERATION ; Steady-state ; STEADY-STATE CONDITIONS ; THERMAL POWER PLANTS</subject><ispartof>IEEE transactions on nuclear science, 1991-04, Vol.38 (2), p.866-872</ispartof><rights>1992 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-eb9035c1ef6839c5d89d7f6b794df67bf6a4b9b05db914320da8110ea023fd783</citedby><cites>FETCH-LOGICAL-c333t-eb9035c1ef6839c5d89d7f6b794df67bf6a4b9b05db914320da8110ea023fd783</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/289402$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,780,784,796,885,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/289402$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=4966064$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/5079670$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Roh Myung-Sub</creatorcontrib><creatorcontrib>Cheon Se-Woo</creatorcontrib><creatorcontrib>Chang Soon-Heung</creatorcontrib><title>Thermal power prediction of nuclear power plant using neural network and parity space model</title><title>IEEE transactions on nuclear science</title><addtitle>TNS</addtitle><description>A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for input preprocessing and a backpropagation network algorithm for network learning are used for the power prediction system. Case studies were performed with emphasis on the applicability of the network in a steady-state high-power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. They also show that the error signals resulting from instrumentation problems can be properly treated even when the signals comprising various patterns are noisy or incomplete.< ></description><subject>220100 - Nuclear Reactor Technology- Theory & Calculation</subject><subject>990200 - Mathematics & Computers</subject><subject>ALGORITHMS</subject><subject>Applied sciences</subject><subject>Artificial neural networks</subject><subject>Backpropagation algorithms</subject><subject>Biological neural networks</subject><subject>Data preprocessing</subject><subject>Energy</subject><subject>Energy. Thermal use of fuels</subject><subject>EQUATIONS</subject><subject>Exact sciences and technology</subject><subject>Fission nuclear power plants</subject><subject>GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE</subject><subject>GENERAL STUDIES OF NUCLEAR REACTORS</subject><subject>HEAT RATE</subject><subject>Installations for energy generation and conversion: thermal and electrical energy</subject><subject>MATHEMATICAL LOGIC</subject><subject>NEURAL NETWORKS</subject><subject>NUCLEAR FACILITIES</subject><subject>NUCLEAR POWER PLANTS</subject><subject>OPERATION</subject><subject>Power generation</subject><subject>POWER PLANTS</subject><subject>Power system modeling</subject><subject>PREDICTION EQUATIONS</subject><subject>Predictive models</subject><subject>REACTOR INSTRUMENTATION</subject><subject>REACTOR OPERATION</subject><subject>Steady-state</subject><subject>STEADY-STATE CONDITIONS</subject><subject>THERMAL POWER PLANTS</subject><issn>0018-9499</issn><issn>1558-1578</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1991</creationdate><recordtype>article</recordtype><recordid>eNo9kM1LHTEUxUNpoa_WRbeugojQxdhk8jHJsojagtCNXbkImeRGU-cl0ySD-N93yjxdXS7ndw6cg9AXSi4oJfpbzy56pTnp36EdFUJ1VAzqPdoRQlWnudYf0ada_6wvF0Ts0P3dI5S9nfCcn6HguYCPrsWccA44LW4CW161yaaGlxrTA06wlNWUoD3n8oRt8ni2JbYXXGfrAO-zh-kz-hDsVOH4cI_Q7-uru8sf3e2vm5-X3287xxhrHYyaMOEoBKmYdsIr7Ycgx0FzH-QwBmn5qEci_KgpZz3xVq1dwZKeBT8odoROt9xcWzTVxQbu0eWUwDUjyKDlQFbofIPmkv8uUJvZx-pgWktBXqrplZRcKL2CXzfQlVxrgWDmEve2vBhKzP-NTc_MtvHKnh1CbXV2CsUmF-ubgWspieQrdrJhEQDe1EPGPxxFg9o</recordid><startdate>19910401</startdate><enddate>19910401</enddate><creator>Roh Myung-Sub</creator><creator>Cheon Se-Woo</creator><creator>Chang Soon-Heung</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>OTOTI</scope></search><sort><creationdate>19910401</creationdate><title>Thermal power prediction of nuclear power plant using neural network and parity space model</title><author>Roh Myung-Sub ; Cheon Se-Woo ; Chang Soon-Heung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-eb9035c1ef6839c5d89d7f6b794df67bf6a4b9b05db914320da8110ea023fd783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1991</creationdate><topic>220100 - Nuclear Reactor Technology- Theory & Calculation</topic><topic>990200 - Mathematics & Computers</topic><topic>ALGORITHMS</topic><topic>Applied sciences</topic><topic>Artificial neural networks</topic><topic>Backpropagation algorithms</topic><topic>Biological neural networks</topic><topic>Data preprocessing</topic><topic>Energy</topic><topic>Energy. Thermal use of fuels</topic><topic>EQUATIONS</topic><topic>Exact sciences and technology</topic><topic>Fission nuclear power plants</topic><topic>GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE</topic><topic>GENERAL STUDIES OF NUCLEAR REACTORS</topic><topic>HEAT RATE</topic><topic>Installations for energy generation and conversion: thermal and electrical energy</topic><topic>MATHEMATICAL LOGIC</topic><topic>NEURAL NETWORKS</topic><topic>NUCLEAR FACILITIES</topic><topic>NUCLEAR POWER PLANTS</topic><topic>OPERATION</topic><topic>Power generation</topic><topic>POWER PLANTS</topic><topic>Power system modeling</topic><topic>PREDICTION EQUATIONS</topic><topic>Predictive models</topic><topic>REACTOR INSTRUMENTATION</topic><topic>REACTOR OPERATION</topic><topic>Steady-state</topic><topic>STEADY-STATE CONDITIONS</topic><topic>THERMAL POWER PLANTS</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Roh Myung-Sub</creatorcontrib><creatorcontrib>Cheon Se-Woo</creatorcontrib><creatorcontrib>Chang Soon-Heung</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>OSTI.GOV</collection><jtitle>IEEE transactions on nuclear science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Roh Myung-Sub</au><au>Cheon Se-Woo</au><au>Chang Soon-Heung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Thermal power prediction of nuclear power plant using neural network and parity space model</atitle><jtitle>IEEE transactions on nuclear science</jtitle><stitle>TNS</stitle><date>1991-04-01</date><risdate>1991</risdate><volume>38</volume><issue>2</issue><spage>866</spage><epage>872</epage><pages>866-872</pages><issn>0018-9499</issn><eissn>1558-1578</eissn><coden>IETNAE</coden><abstract>A power prediction system was developed using an artificial neural network paradigm that was combined with a parity space signal validation technique. The parity space signal validation algorithm for input preprocessing and a backpropagation network algorithm for network learning are used for the power prediction system. Case studies were performed with emphasis on the applicability of the network in a steady-state high-power level. The studies reveal that these algorithms can precisely predict the thermal power in a nuclear power plant. They also show that the error signals resulting from instrumentation problems can be properly treated even when the signals comprising various patterns are noisy or incomplete.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/23.289402</doi><tpages>7</tpages></addata></record> |
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| identifier | ISSN: 0018-9499 |
| ispartof | IEEE transactions on nuclear science, 1991-04, Vol.38 (2), p.866-872 |
| issn | 0018-9499 1558-1578 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | 220100 - Nuclear Reactor Technology- Theory & Calculation 990200 - Mathematics & Computers ALGORITHMS Applied sciences Artificial neural networks Backpropagation algorithms Biological neural networks Data preprocessing Energy Energy. Thermal use of fuels EQUATIONS Exact sciences and technology Fission nuclear power plants GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE GENERAL STUDIES OF NUCLEAR REACTORS HEAT RATE Installations for energy generation and conversion: thermal and electrical energy MATHEMATICAL LOGIC NEURAL NETWORKS NUCLEAR FACILITIES NUCLEAR POWER PLANTS OPERATION Power generation POWER PLANTS Power system modeling PREDICTION EQUATIONS Predictive models REACTOR INSTRUMENTATION REACTOR OPERATION Steady-state STEADY-STATE CONDITIONS THERMAL POWER PLANTS |
| title | Thermal power prediction of nuclear power plant using neural network and parity space model |
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