Methods for online voltage stability monitoring

dc.contributor.advisor Venkataramana Ajjarapu
dc.contributor.author Karki, Mahesh
dc.contributor.department Electrical and Computer Engineering
dc.date 2018-08-11T07:06:31.000
dc.date.accessioned 2020-06-30T02:33:12Z
dc.date.available 2020-06-30T02:33:12Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2013-06-05
dc.date.issued 2009-01-01
dc.description.abstract <p>Online voltage stability monitoring is the process of obtaining voltage stability information in real time for a given operating condition. The prediction should be fast and accurate so that control signals can be sent to appropriate locations quickly and effectively. One approach is to get the stability information directly from the phasor measurements obtained for operating conditions. This approach is simple and requires few computations. The methods proposed are based on Thyvenin equivalent of a system. The Thyvenin equivalent, according to the maximum power transfer theorem,gives the upper limit of the power transfer to a load bus. To get the Thyvenin equivalent we need at least two sets of phasor measurements. It is found that Thyvenin equivalent gives a highly optimistic approximation of power margin. The work done in this thesis compensates the optimistic prediction by applying reactive power availability information of the system.The accuracy of this approach is very high compared to Thevenin equivalent. The thesis also presents improvement on decision trees method for online voltage stability monitoring by attribute selection. The role of data mining approach such as decision tree is vital in using the available accurate measurement data in the power system. Also, it is very important to extract important data or attributes so that the tree is robust, reliable and easy to compute. Data mining itself offers information based (gain ratio), statistical (k-nearest neighbor), probabilistic (nayve Bayes) and others for attribute selection. There are analytical approaches in power systems which can characterize attributes as well. Can these attributes be used for attribute selection for decision trees? The hypothesis has been tested using the tangent vector information of attributes. The accuracy of the selected attributes on decision trees is very high. Attributes with higher sensitivity were found to be better indicators of voltage instability. Attribute selection will be very helpful when it comes to large systems with a huge volume of data.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/11086/
dc.identifier.articleid 2089
dc.identifier.contextkey 2807287
dc.identifier.doi https://doi.org/10.31274/etd-180810-1584
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/11086
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/25292
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/11086/Karki_iastate_0097M_10908.pdf|||Fri Jan 14 18:41:48 UTC 2022
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.keywords Attribute Selection
dc.subject.keywords Decision Trees
dc.subject.keywords Reactive Reserves
dc.subject.keywords Tangent Vector
dc.subject.keywords Thevenin Equivalent
dc.subject.keywords Voltage Control Area
dc.title Methods for online voltage stability monitoring
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.level thesis
thesis.degree.name Master of Science
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