Coherent grouping of power systems for use in training artificial neural networks
This paper presents a methodology for applying artificial neural networks to power systems of various sizes while addressing the problem of increasing training set size with increasing power system size. A slow-coherency based network partitioning technique is used to group the generators and load b...
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| creator | McFarlane, A.S. Alden, R.T.H. |
| description | This paper presents a methodology for applying artificial neural networks to power systems of various sizes while addressing the problem of increasing training set size with increasing power system size. A slow-coherency based network partitioning technique is used to group the generators and load buses of the 10-machine, 39-bus system into coherent areas. Next we use characteristic parameters of each area as input features to train and perform estimations using a feed-forward neural network.< > |
| doi_str_mv | 10.1109/MWSCAS.1993.342949 |
| format | Conference Proceeding |
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A slow-coherency based network partitioning technique is used to group the generators and load buses of the 10-machine, 39-bus system into coherent areas. 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Next we use characteristic parameters of each area as input features to train and perform estimations using a feed-forward neural network.< ></description><subject>Analytical models</subject><subject>Artificial neural networks</subject><subject>Circuit faults</subject><subject>Circuit simulation</subject><subject>Feature extraction</subject><subject>Intelligent networks</subject><subject>Power generation</subject><subject>Power system simulation</subject><subject>Power system transients</subject><subject>Power systems</subject><isbn>9780780317604</isbn><isbn>0780317602</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1993</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj9tKAzEYhAMiKHVfoFd5gV3zb7KHXJbFE1REqnhZks2fGm03S5Kl9O1drcPANxfDwBCyBFYAMHn7_LHpVpsCpOQFF6UU8oJksmnZbA5NzcQVyWL8YrNExRomrslr5z8x4JDoLvhpdMOOektHf8RA4ykmPERqfaBTROoGmoJyw29JheSs653a0wGn8Id09OE73pBLq_YRs38uyPv93Vv3mK9fHp661Tp3wETK21oA9K22ZWNrpVFwwTWrLNSAWvSmEqANb2tTNdiDttgyw1GWJSsNmDkvyPK86xBxOwZ3UOG0Pf_mPyCZT9Q</recordid><startdate>1993</startdate><enddate>1993</enddate><creator>McFarlane, A.S.</creator><creator>Alden, R.T.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1993</creationdate><title>Coherent grouping of power systems for use in training artificial neural networks</title><author>McFarlane, A.S. ; Alden, R.T.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-86411c8bf27f6abe4343b05f161eb4cd541bd386d57ec1bfe80d3e92202d1d0d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Analytical models</topic><topic>Artificial neural networks</topic><topic>Circuit faults</topic><topic>Circuit simulation</topic><topic>Feature extraction</topic><topic>Intelligent networks</topic><topic>Power generation</topic><topic>Power system simulation</topic><topic>Power system transients</topic><topic>Power systems</topic><toplevel>online_resources</toplevel><creatorcontrib>McFarlane, A.S.</creatorcontrib><creatorcontrib>Alden, R.T.H.</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 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>McFarlane, A.S.</au><au>Alden, R.T.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Coherent grouping of power systems for use in training artificial neural networks</atitle><btitle>Proceedings of 36th Midwest Symposium on Circuits and Systems</btitle><stitle>MWSCAS</stitle><date>1993</date><risdate>1993</risdate><spage>704</spage><epage>707 vol.1</epage><pages>704-707 vol.1</pages><isbn>9780780317604</isbn><isbn>0780317602</isbn><abstract>This paper presents a methodology for applying artificial neural networks to power systems of various sizes while addressing the problem of increasing training set size with increasing power system size. A slow-coherency based network partitioning technique is used to group the generators and load buses of the 10-machine, 39-bus system into coherent areas. Next we use characteristic parameters of each area as input features to train and perform estimations using a feed-forward neural network.< ></abstract><pub>IEEE</pub><doi>10.1109/MWSCAS.1993.342949</doi></addata></record> |
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| identifier | ISBN: 9780780317604 |
| ispartof | Proceedings of 36th Midwest Symposium on Circuits and Systems, 1993, p.704-707 vol.1 |
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| language | eng |
| recordid | cdi_ieee_primary_342949 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Analytical models Artificial neural networks Circuit faults Circuit simulation Feature extraction Intelligent networks Power generation Power system simulation Power system transients Power systems |
| title | Coherent grouping of power systems for use in training artificial neural networks |
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