Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods
Power-quality voltage and current transient waveform data have been explored rather extensively as the primary input data in predictive maintenance, automatic root-cause analysis, and evaluating system performance to indicate potential problems. Unfortunately, very few efforts have been directed tow...
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| Veröffentlicht in: | IEEE transactions on power delivery 2008-01, Vol.23 (1), p.380-388 |
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| container_title | IEEE transactions on power delivery |
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| creator | Mago, N.V. Santoso, S. McGranaghan, M.F. |
| description | Power-quality voltage and current transient waveform data have been explored rather extensively as the primary input data in predictive maintenance, automatic root-cause analysis, and evaluating system performance to indicate potential problems. Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data. |
| doi_str_mv | 10.1109/TPWRD.2007.905549 |
| format | Article |
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Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data.</description><identifier>ISSN: 0885-8977</identifier><identifier>EISSN: 1937-4208</identifier><identifier>DOI: 10.1109/TPWRD.2007.905549</identifier><identifier>CODEN: ITPDE5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Assessments ; Control ; Disturbances. Regulation. 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Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data.</description><subject>Applied sciences</subject><subject>Assessments</subject><subject>Control</subject><subject>Disturbances. Regulation. Protection</subject><subject>Effectiveness</subject><subject>Electric potential</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical machines</subject><subject>Electrical power engineering</subject><subject>Exact sciences and technology</subject><subject>Feeders</subject><subject>Operation. Load control. Reliability</subject><subject>Performance analysis</subject><subject>Power electronics, power supplies</subject><subject>Power networks and lines</subject><subject>Power quality</subject><subject>Power quality (PQ)</subject><subject>Predictive maintenance</subject><subject>Process control</subject><subject>Regulation and control</subject><subject>Regulators</subject><subject>Statistical analysis</subject><subject>Statistical process control</subject><subject>statistics</subject><subject>Steady-state</subject><subject>System performance</subject><subject>Transient analysis</subject><subject>Voltage</subject><subject>Voltage control</subject><subject>Waveforms</subject><issn>0885-8977</issn><issn>1937-4208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkUFvEzEQhS1EpYbAD6i4WEjAacN4ba_tYxUoVGrVUlo4Wo53HLbarFvbOfDv65CqSBzgNBrN92Y07xFyxGDBGJgP15c_rj4uWgC1MCClMM_IjBmuGtGCfk5moLVstFHqkLzI-RYABBiYka_HOWPOG5wKjYGeIPaY6Pc4FrdGeoXr7ejKECd6k4dpTb-V2uUyeDfSyxR9ldJlnEqKIz3H8jP2-SU5CG7M-OqxzsnNyafr5Zfm7OLz6fL4rPGCqdK0veAKlRMMuJesD51XmjG2ao3pGQYPwrOg-h5khy1T2qyCMnrVBq086xSfk_f7vXcp3m8xF7sZssdxdBPGbbYGeCck1_BfUisJvJ42lXz3T5IL0XU7ek7e_AXexm2a6r9WdxyAadVWiO0hn2LOCYO9S8PGpV-Wgd2lZn-nZnep2X1qVfP2cbHL1eSQ3OSH_EdojOSC68q93nMDIj6Nq6W61ZI_AKRCnw8</recordid><startdate>200801</startdate><enddate>200801</enddate><creator>Mago, N.V.</creator><creator>Santoso, S.</creator><creator>McGranaghan, M.F.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Electrical power engineering</topic><topic>Electrical machines</topic><topic>Electrical power engineering</topic><topic>Exact sciences and technology</topic><topic>Feeders</topic><topic>Operation. Load control. Reliability</topic><topic>Performance analysis</topic><topic>Power electronics, power supplies</topic><topic>Power networks and lines</topic><topic>Power quality</topic><topic>Power quality (PQ)</topic><topic>Predictive maintenance</topic><topic>Process control</topic><topic>Regulation and control</topic><topic>Regulators</topic><topic>Statistical analysis</topic><topic>Statistical process control</topic><topic>statistics</topic><topic>Steady-state</topic><topic>System performance</topic><topic>Transient analysis</topic><topic>Voltage</topic><topic>Voltage control</topic><topic>Waveforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mago, N.V.</creatorcontrib><creatorcontrib>Santoso, S.</creatorcontrib><creatorcontrib>McGranaghan, M.F.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power delivery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mago, N.V.</au><au>Santoso, S.</au><au>McGranaghan, M.F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods</atitle><jtitle>IEEE transactions on power delivery</jtitle><stitle>TPWRD</stitle><date>2008-01</date><risdate>2008</risdate><volume>23</volume><issue>1</issue><spage>380</spage><epage>388</epage><pages>380-388</pages><issn>0885-8977</issn><eissn>1937-4208</eissn><coden>ITPDE5</coden><abstract>Power-quality voltage and current transient waveform data have been explored rather extensively as the primary input data in predictive maintenance, automatic root-cause analysis, and evaluating system performance to indicate potential problems. Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TPWRD.2007.905549</doi><tpages>9</tpages></addata></record> |
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| ispartof | IEEE transactions on power delivery, 2008-01, Vol.23 (1), p.380-388 |
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| subjects | Applied sciences Assessments Control Disturbances. Regulation. Protection Effectiveness Electric potential Electrical engineering. Electrical power engineering Electrical machines Electrical power engineering Exact sciences and technology Feeders Operation. Load control. Reliability Performance analysis Power electronics, power supplies Power networks and lines Power quality Power quality (PQ) Predictive maintenance Process control Regulation and control Regulators Statistical analysis Statistical process control statistics Steady-state System performance Transient analysis Voltage Voltage control Waveforms |
| title | Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods |
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