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dc.contributor.advisorSingh, Chananen_US
dc.creatorLiu, Yongen_US
dc.date.accessioned2011-02-22T22:24:33Zen_US
dc.date.accessioned2011-02-22T23:49:39Z
dc.date.available2011-02-22T22:24:33Zen_US
dc.date.available2011-02-22T23:49:39Z
dc.date.created2010-12en_US
dc.date.issued2011-02-22en_US
dc.date.submittedDecember 2010en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8716en_US
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_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectreliabilityen_US
dc.subjectcomposite power systemen_US
dc.subjecthurricaneen_US
dc.subjectfuzzy inference systemen_US
dc.subjectcommon-cause failureen_US
dc.subjectBayesian networken_US
dc.subjectminimal cut-seten_US
dc.subjectMonte Carlo simulationen_US
dc.titleReliability Evaluation of Composite Power Systems Including the Effects of Hurricanesen_US
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.levelDoctoralen_US
dc.contributor.committeeMemberHuang, Garng M.en_US
dc.contributor.committeeMemberSprintson, Alexen_US
dc.contributor.committeeMemberGautam, Natarajanen_US
dc.type.genreElectronic Dissertationen_US
dc.type.materialtexten_US


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