Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/343019
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dc.coverage.spatialIntelligent fault location isolation and service restoration in distribution system
dc.date.accessioned2021-10-04T09:24:27Z-
dc.date.available2021-10-04T09:24:27Z-
dc.identifier.urihttp://hdl.handle.net/10603/343019-
dc.description.abstractThe upsurge in the demand for power has increased the size and scale of the power infrastructure. De-regularization of the electrical industry has contributed to the increased complexity of the system. Faults are inevitable in such a complex system and can occur at any part of the power system starting from power generation up to the end consumption. If faults are not swiftly identified and isolated, they can lead to cascading faults eventually leading to large scale breakdown of the system. With the advancements of technology, the prospect for reliable power supply and swift service restoration after a fault in a power distribution system has also increased. Competing electric utilities aim to cater power at higher reliability to their customers. This is possible through efficient fault identification, isolation and quick service restoration. The objective of this research work is to address the problem of Fault Location, Isolation and Service Restoration (FLISR) in power distribution system. First, a fault diagnosis solution is proposed to identify fault at the feeders connected to a bus in a distribution substation. The solution is realized using Bayesian probabilistic matrix. The limitations of rule-based systems in handling uncertainties and that of regression-based systems in complex cause effect relationship is jointly overcome by using Bayesian approach which are graphical models that can handle complex, causal problems that suffer from uncertainties. The uncertainty during a fault is the non-operation or maloperation of protective device or a loss of information about the status of the protective devices due to communication problems. A directed acyclic graph (DAG) is used to represent the power system under consideration. Looking at the DAG, Conditional Probabilistic Distribution (CPD) tables are formed I based on which the Bayesian Probabilistic matrix is derived. Also, few relevant vectors are defined. Applying fuzzy operators on the derived matrix and vectors, the faulty feeder is correctly identifie
dc.format.extentxxii,153 p.
dc.languageEnglish
dc.relationp.143-152
dc.rightsuniversity
dc.titleIntelligent fault location isolation and service restoration in distribution system
dc.title.alternative
dc.creator.researcherIndhumathi, C
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Electrical and Electronic
dc.subject.keywordDistribution system
dc.subject.keywordFault location
dc.subject.keywordService restoration
dc.description.note
dc.contributor.guideJoy Vasantha Rani, S P
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2019
dc.date.awarded2019
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

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01_title.pdfAttached File236.54 kBAdobe PDFView/Open
02_certificates.pdf196.5 kBAdobe PDFView/Open
03_vivaproceedings.pdf631.41 kBAdobe PDFView/Open
04_bonafidecertificate.pdf71.6 kBAdobe PDFView/Open
05_abstracts.pdf184.93 kBAdobe PDFView/Open
06_acknowledgements.pdf446.35 kBAdobe PDFView/Open
07_contents.pdf202.14 kBAdobe PDFView/Open
08_listoftables.pdf175.34 kBAdobe PDFView/Open
09_listoffigures.pdf184.62 kBAdobe PDFView/Open
10_listofabbreviations.pdf309.36 kBAdobe PDFView/Open
11_chapter1.pdf604.47 kBAdobe PDFView/Open
12_chapter2.pdf328.5 kBAdobe PDFView/Open
13_chapter3.pdf1.98 MBAdobe PDFView/Open
14_chapter4.pdf3.27 MBAdobe PDFView/Open
15_chapter5.pdf3.24 MBAdobe PDFView/Open
16_conclusion.pdf221.14 kBAdobe PDFView/Open
17_appendices.pdf221.43 kBAdobe PDFView/Open
18_references.pdf343.35 kBAdobe PDFView/Open
19_listofpublications.pdf206.71 kBAdobe PDFView/Open
80_recommendation.pdf340.41 kBAdobe PDFView/Open


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