Analysis of SSR using artificial neural networks [power system simulation]
Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance...
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| creator | Nagabhushana, B.S. Chandrasekharaiah, H.S. |
| description | Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the entire operating range. The effectiveness of this approach is tested by experimenting on the first bench mark model proposed by IEEE Task Force on SSR. |
| doi_str_mv | 10.1109/ISAP.1996.501109 |
| format | Conference Proceeding |
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They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the entire operating range. The effectiveness of this approach is tested by experimenting on the first bench mark model proposed by IEEE Task Force on SSR.</description><subject>Artificial neural networks</subject><subject>Capacitance</subject><subject>Capacitors</subject><subject>Inductors</subject><subject>Neural networks</subject><subject>Power systems</subject><subject>Reactive power</subject><subject>Rotors</subject><subject>Static VAr compensators</subject><subject>Thyristors</subject><isbn>078033115X</isbn><isbn>9780780331150</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jr0KwjAYRQMi-NddnPIC1sRYtWMRRZ3EOggiJUgqn6ZJyZcifXt_Z89y4NzlEtLnLOScxaNNmuxCHsfTMGLv0CAdNpszITiPji0SIN7Yi0nEx1y0yTYxUtcISG1O03RPKwRzpdJ5yOECUlOjKveRf1h3R3oq7UM5ijV6VVCEotLSgzXnHmnmUqMKfu6SwWp5WKyHoJTKSgeFdHX2fSX-jk_aCzx6</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Nagabhushana, B.S.</creator><creator>Chandrasekharaiah, H.S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>Analysis of SSR using artificial neural networks [power system simulation]</title><author>Nagabhushana, B.S. ; Chandrasekharaiah, H.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_5011093</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Artificial neural networks</topic><topic>Capacitance</topic><topic>Capacitors</topic><topic>Inductors</topic><topic>Neural networks</topic><topic>Power systems</topic><topic>Reactive power</topic><topic>Rotors</topic><topic>Static VAr compensators</topic><topic>Thyristors</topic><toplevel>online_resources</toplevel><creatorcontrib>Nagabhushana, B.S.</creatorcontrib><creatorcontrib>Chandrasekharaiah, H.S.</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>Nagabhushana, B.S.</au><au>Chandrasekharaiah, H.S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Analysis of SSR using artificial neural networks [power system simulation]</atitle><btitle>Proceedings of International Conference on Intelligent System Application to Power Systems</btitle><stitle>ISAP</stitle><date>1996</date><risdate>1996</risdate><spage>416</spage><epage>420</epage><pages>416-420</pages><isbn>078033115X</isbn><isbn>9780780331150</isbn><abstract>Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the entire operating range. The effectiveness of this approach is tested by experimenting on the first bench mark model proposed by IEEE Task Force on SSR.</abstract><pub>IEEE</pub><doi>10.1109/ISAP.1996.501109</doi></addata></record> |
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| identifier | ISBN: 078033115X |
| ispartof | Proceedings of International Conference on Intelligent System Application to Power Systems, 1996, p.416-420 |
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| language | eng |
| recordid | cdi_ieee_primary_501109 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Capacitance Capacitors Inductors Neural networks Power systems Reactive power Rotors Static VAr compensators Thyristors |
| title | Analysis of SSR using artificial neural networks [power system simulation] |
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