A context based data sanity checking algorithm and its implementation

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dc.contributor.authorLahouar, Saheren
dc.contributor.committeechairRahman, Saifuren
dc.contributor.committeememberBroadwater, Robert P.en
dc.contributor.committeememberDe La Ree, Jaimeen
dc.contributor.committeememberSullivan, William G.en
dc.contributor.committeememberVanLandingham, Hugh F.en
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2014-03-14T21:08:59Zen
dc.date.adate2006-02-01en
dc.date.available2014-03-14T21:08:59Zen
dc.date.issued1991-12-05en
dc.date.rdate2006-02-01en
dc.date.sdate2006-02-01en
dc.description.abstractIn this dissertation, we present a cost-effective, neural network-based technique for data sanity checking and small system parameter monitoring which utilizes the contextual information in which data is collected to avoid the need for multiple metering. Multiple metering is not always a feasible nor an optimal solution to the problem. In an environment where it is necessary to monitor a large number of different physical variables, the mere installation and maintenance of multiple metering equipment can prove to be very costly. Moreover, multiple measurements of the same quantity result in a phenomenon known as data explosion. Context-based sensoyvalidation is achieved through cross sensor redundancy, which is not to be confused with metering redundancy. Neural networks are used to model the relationships among the various parameters and to provide context-based estimates which help in identifying sensor (versus system) malfunction. Slow tracking of the relationships among the parameters as they change over time is made possible through on-line training of the neural networks on the most recent data. This helps to account for the dependency of the relationships among system parameters on the range of external variables such as ambient temperature. A prototypical system titled DASANEX is implemented to illustrate the validity of the technique. The system is used to monitor and filter real-time transformer and ambient temperature data. A proof-of-concept is established using field data from the city of Martinsville Electric Department. Results prove the superior ability of the technique to identify sensor malfunction and to provide real-time adequate replacement values during short downtimes of the sensors even when some sensor data are missing or contaminated.en
dc.description.degreePh. D.en
dc.format.extentxi, 144 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-02012006-141731en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-02012006-141731/en
dc.identifier.urihttp://hdl.handle.net/10919/37248en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1991.L346.pdfen
dc.relation.isformatofOCLC# 27880830en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1991.L346en
dc.subject.lcshNeural networks (Computer science)en
dc.subject.lcshQuality factor metersen
dc.titleA context based data sanity checking algorithm and its implementationen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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