Show simple item record

dc.contributor.advisorSingh, Chanan
dc.creatorLiu, Yong
dc.date.accessioned2011-02-22T22:24:33Z
dc.date.accessioned2011-02-22T23:49:39Z
dc.date.available2011-02-22T22:24:33Z
dc.date.available2011-02-22T23:49:39Z
dc.date.created2010-12
dc.date.issued2011-02-22
dc.date.submittedDecember 2010
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8716
dc.description.abstractAdverse weather such as hurricanes can significantly affect the reliability of composite power systems. Predicting the impact of hurricanes can help utilities for better preparedness and make appropriate restoration arrangements. In this dissertation, the impact of hurricanes on the reliability of composite power systems is investigated. Firstly, the impact of adverse weather on the long-term reliability of composite power systems is investigated by using Markov cut-set method. The Algorithms for the implementation is developed. Here, two-state weather model is used. An algorithm for sequential simulation is also developed to achieve the same goal. The results obtained by using the two methods are compared. The comparison shows that the analytical method can obtain comparable results and meantime it can be faster than the simulation method. Secondly, the impact of hurricanes on the short-term reliability of composite power systems is investigated. A fuzzy inference system is used to assess the failure rate increment of system components. Here, different methods are used to build two types of fuzzy inference systems. Considering the fact that hurricanes usually last only a few days, short-term minimal cut-set method is proposed to compute the time-specific system and nodal reliability indices of composite power systems. The implementation demonstrates that the proposed methodology is effective and efficient and is flexible in its applications. Thirdly, the impact of hurricanes on the short-term reliability of composite power systems including common-cause failures is investigated. Here, two methods are proposed to archive this goal. One of them uses a Bayesian network to alleviate the dimensionality problem of conditional probability method. Another method extends minimal cut-set method to accommodate common-cause failures. The implementation results obtained by using the two methods are compared and their discrepancy is analyzed. Finally, the proposed methods in this dissertation are also applicable to other applications in power systems.en
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.subjectreliabilityen
dc.subjectcomposite power systemen
dc.subjecthurricaneen
dc.subjectfuzzy inference systemen
dc.subjectcommon-cause failureen
dc.subjectBayesian networken
dc.subjectminimal cut-seten
dc.subjectMonte Carlo simulationen
dc.titleReliability Evaluation of Composite Power Systems Including the Effects of Hurricanesen
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberHuang, Garng M.
dc.contributor.committeeMemberSprintson, Alex
dc.contributor.committeeMemberGautam, Natarajan
dc.type.genreElectronic Dissertationen
dc.type.materialtexten


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record