Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system
This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depend...
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creator | Wahab, N.I.A. Mohamed, A. Hussain, A. |
description | This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM. |
doi_str_mv | 10.1109/PECON.2008.4762523 |
format | Conference Proceeding |
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The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.</description><identifier>ISBN: 1424424046</identifier><identifier>ISBN: 9781424424047</identifier><identifier>EISBN: 1424424054</identifier><identifier>EISBN: 9781424424054</identifier><identifier>DOI: 10.1109/PECON.2008.4762523</identifier><identifier>LCCN: 2008903276</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Large-scale systems ; Least squares methods ; least squares support vector machine ; Neural networks ; Power generation ; Power system faults ; Power system simulation ; Power system stability ; Power system transients ; probabilistic neural network ; Support vector machines ; transient stability assessment</subject><ispartof>2008 IEEE 2nd International Power and Energy Conference, 2008, p.485-489</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4762523$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4762523$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wahab, N.I.A.</creatorcontrib><creatorcontrib>Mohamed, A.</creatorcontrib><creatorcontrib>Hussain, A.</creatorcontrib><title>Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system</title><title>2008 IEEE 2nd International Power and Energy Conference</title><addtitle>PECON</addtitle><description>This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.</description><subject>Artificial neural networks</subject><subject>Large-scale systems</subject><subject>Least squares methods</subject><subject>least squares support vector machine</subject><subject>Neural networks</subject><subject>Power generation</subject><subject>Power system faults</subject><subject>Power system simulation</subject><subject>Power system stability</subject><subject>Power system transients</subject><subject>probabilistic neural network</subject><subject>Support vector machines</subject><subject>transient stability assessment</subject><isbn>1424424046</isbn><isbn>9781424424047</isbn><isbn>1424424054</isbn><isbn>9781424424054</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFUM1KAzEYjEhBW_sCeskLbE022U1ylKX-QLEe9Fyyu19qdP_Ml1r6FL6yWxUcBoYZhjkMIZecLThn5vppWawfFyljeiFVnmapOCFTLlM5kmXy9N_IfEKmx6JhIlX5GZkjvrERMhO5MOfkq-jbwQbfbWkDFiPFj50NgBR3w9CHSD-hin2gra1efQfUdjUdQl_a0jceo69oB7tgm1Hivg_v1Hc0Btuhh24ciz-9eKAWERDbY9g7amljwxYSrGwDdOj3ECgeMEJ7QSbONgjzP52Rl9vlc3GfrNZ3D8XNKvFcZTER0mgLykDlmHHjKVqakjteG1WZVNdcgHYl2EzVpZYgtGaVKzWo2jAnlRIzcvW76wFgMwTf2nDY_L0pvgFHGWtb</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Wahab, N.I.A.</creator><creator>Mohamed, A.</creator><creator>Hussain, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system</title><author>Wahab, N.I.A. ; Mohamed, A. ; Hussain, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-3498ae79ecf09f110849b1f1d97c928d13e8fbea57db84e3880cfb8e7d90f4773</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial neural networks</topic><topic>Large-scale systems</topic><topic>Least squares methods</topic><topic>least squares support vector machine</topic><topic>Neural networks</topic><topic>Power generation</topic><topic>Power system faults</topic><topic>Power system simulation</topic><topic>Power system stability</topic><topic>Power system transients</topic><topic>probabilistic neural network</topic><topic>Support vector machines</topic><topic>transient stability assessment</topic><toplevel>online_resources</toplevel><creatorcontrib>Wahab, N.I.A.</creatorcontrib><creatorcontrib>Mohamed, A.</creatorcontrib><creatorcontrib>Hussain, A.</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>Wahab, N.I.A.</au><au>Mohamed, A.</au><au>Hussain, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system</atitle><btitle>2008 IEEE 2nd International Power and Energy Conference</btitle><stitle>PECON</stitle><date>2008-12</date><risdate>2008</risdate><spage>485</spage><epage>489</epage><pages>485-489</pages><isbn>1424424046</isbn><isbn>9781424424047</isbn><eisbn>1424424054</eisbn><eisbn>9781424424054</eisbn><abstract>This paper presents transient stability assessment of a large practical power system using two artificial neural network techniques which are the probabilistic neural network (PNN) and the least squares support vector machine (LS-SVM). The large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the number of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the test system considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN and LS-SVM. Both networks are used as classifiers to determine whether the power system is stable or unstable. Classification results show that the PNN gives faster and more accurate transient stability assessment compared to the LS-SVM.</abstract><pub>IEEE</pub><doi>10.1109/PECON.2008.4762523</doi><tpages>5</tpages></addata></record> |
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ispartof | 2008 IEEE 2nd International Power and Energy Conference, 2008, p.485-489 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Artificial neural networks Large-scale systems Least squares methods least squares support vector machine Neural networks Power generation Power system faults Power system simulation Power system stability Power system transients probabilistic neural network Support vector machines transient stability assessment |
title | Comparing least squares support vector machine and probabilistic neural network in transient stability assessment of a large-scale power system |
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