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dc.contributorFaculty of Engineeringen_US
dc.contributor.advisorXu, Zhao (EE)-
dc.creatorZhang, Yunpeng-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/8897-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleEvaluation of the holomorphic embedding method for contingency analysisen_US
dcterms.abstractMost successful power-flow (PF) methods in the industry are based on iterative techniques, which can converge to the desired solution with a relatively rapid speed. However, they have inherent convergence problems, which are not guaranteed to derive the accurate solution. A novel non-iterative method developed recently [1], the Holomorphic Embedding Load Flow Method (HELM), developed by Dr.Trias is guaranteed to yield the correct solution or signal voltage collapse. In contingency analysis simulation, it is very common that system operates near or beyond the voltage collapse point. As PF calculation is a fundamental procedure integrated in contingency analysis, the ability to derive a determined solution is critical important. The performance of HELM when system operates near and beyond the voltage collapse point is evaluated with several test systems with MATLAB. A criteria for the existence of solution is proposed in this thesis, which is not constrained by precision. Holomorphic embedding and analytic continuation are two key techniques, which are applied in HELM. In theory, there are infinity ways of holomorphic embedding, key points of which are illustrated during the basic holomorphic embedding model constructing process. Analytic continuation, property of holomorphic function power series, is explained from a simple but basic perspective.en_US
dcterms.extentviii, 72 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2016en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHHolomorphic functions.en_US
dcterms.LCSHElectric power systems -- Reliability.en_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/8897