Algorithms for least median of squares state estimation of power systems
The least median of squares (LMS) estimator minimizes the vth ordered squared residual. The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers that any regression equivariant estimator can handle. This frac...
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| creator | Mili, L. Cheniae, M.G. Vichare, N.S. Rousseeuw, P.J. |
| description | The least median of squares (LMS) estimator minimizes the vth ordered squared residual. The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers that any regression equivariant estimator can handle. This fraction is equal to half of the minimum surplus divided by the number of measurements in the network. The surplus of a fundamental set is defined as the smallest number of measurements whose removal from that fundamental set turns at least one measurement in the network into a critical one. Based on the surplus concept, a system decomposition scheme that significantly increases the number of outliers that can be identified by the LMS is developed. In addition, it dramatically reduces the computing time of the LMS, opening the door to real-time applications of that estimator to large-scale systems. Finally, outlier diagnostics based on robust Mahalanobis distances are proposed.< > |
| doi_str_mv | 10.1109/MWSCAS.1992.271039 |
| format | Conference Proceeding |
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The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers that any regression equivariant estimator can handle. This fraction is equal to half of the minimum surplus divided by the number of measurements in the network. The surplus of a fundamental set is defined as the smallest number of measurements whose removal from that fundamental set turns at least one measurement in the network into a critical one. Based on the surplus concept, a system decomposition scheme that significantly increases the number of outliers that can be identified by the LMS is developed. In addition, it dramatically reduces the computing time of the LMS, opening the door to real-time applications of that estimator to large-scale systems. Finally, outlier diagnostics based on robust Mahalanobis distances are proposed.< ></description><subject>Instruments</subject><subject>Least squares approximation</subject><subject>Pollution measurement</subject><subject>Power measurement</subject><subject>Power system measurements</subject><subject>Power system modeling</subject><subject>Power system reliability</subject><subject>Power systems</subject><subject>Robustness</subject><subject>State estimation</subject><isbn>9780780305106</isbn><isbn>0780305108</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1992</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj91Kw0AUhBdEUGpeoFf7Aon7k589lyGoFVq8qOJl2STnaCQxdc-K9O0N1mFgLj4YZoRYa5VpreB297pv6n2mAUxmKq0sXIgEKqcWW1VoVV6JhPlDLcoLrQ1ci009vs1hiO8TS5qDHNFzlBP2g_-UM0n--vYBWXL0ESVyHCYfh_mPHecfDJJPHHHiG3FJfmRM_nMlXu7vnptNun16eGzqbdoZY2NqKt97yqlSZUfKUW7botO967BsAYGcL6GlzmhVYOsBHKHLnXZFXlGLhuxKrM-9AyIejmHZE06H8137C_WBTPE</recordid><startdate>1992</startdate><enddate>1992</enddate><creator>Mili, L.</creator><creator>Cheniae, M.G.</creator><creator>Vichare, N.S.</creator><creator>Rousseeuw, P.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1992</creationdate><title>Algorithms for least median of squares state estimation of power systems</title><author>Mili, L. ; Cheniae, M.G. ; Vichare, N.S. ; Rousseeuw, P.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-27adaf4f706cf08f43b5c1d8ce6b9e9f8a69bfc2105eba998fe84818547fbe2f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Instruments</topic><topic>Least squares approximation</topic><topic>Pollution measurement</topic><topic>Power measurement</topic><topic>Power system measurements</topic><topic>Power system modeling</topic><topic>Power system reliability</topic><topic>Power systems</topic><topic>Robustness</topic><topic>State estimation</topic><toplevel>online_resources</toplevel><creatorcontrib>Mili, L.</creatorcontrib><creatorcontrib>Cheniae, M.G.</creatorcontrib><creatorcontrib>Vichare, N.S.</creatorcontrib><creatorcontrib>Rousseeuw, P.J.</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>Mili, L.</au><au>Cheniae, M.G.</au><au>Vichare, N.S.</au><au>Rousseeuw, P.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Algorithms for least median of squares state estimation of power systems</atitle><btitle>[1992] Proceedings of the 35th Midwest Symposium on Circuits and Systems</btitle><stitle>MWSCAS</stitle><date>1992</date><risdate>1992</risdate><spage>1276</spage><epage>1283 vol.2</epage><pages>1276-1283 vol.2</pages><isbn>9780780305106</isbn><isbn>0780305108</isbn><abstract>The least median of squares (LMS) estimator minimizes the vth ordered squared residual. The authors derived a general expression of the optimal v for which the breakdown point of the LMS attains the highest possible fraction of outliers that any regression equivariant estimator can handle. This fraction is equal to half of the minimum surplus divided by the number of measurements in the network. The surplus of a fundamental set is defined as the smallest number of measurements whose removal from that fundamental set turns at least one measurement in the network into a critical one. Based on the surplus concept, a system decomposition scheme that significantly increases the number of outliers that can be identified by the LMS is developed. In addition, it dramatically reduces the computing time of the LMS, opening the door to real-time applications of that estimator to large-scale systems. Finally, outlier diagnostics based on robust Mahalanobis distances are proposed.< ></abstract><pub>IEEE</pub><doi>10.1109/MWSCAS.1992.271039</doi></addata></record> |
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| identifier | ISBN: 9780780305106 |
| ispartof | [1992] Proceedings of the 35th Midwest Symposium on Circuits and Systems, 1992, p.1276-1283 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_271039 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Instruments Least squares approximation Pollution measurement Power measurement Power system measurements Power system modeling Power system reliability Power systems Robustness State estimation |
| title | Algorithms for least median of squares state estimation of power systems |
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