Please use this identifier to cite or link to this item: `http://hdl.handle.net/10397/85522`
DC FieldValueLanguage
dc.contributorDepartment of Electrical Engineering-
dc.creatorZhang, Jianfen-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/5335-
dc.language.isoEnglish-
dc.titleVoltage stability analysis based on probability theory-
dc.typeThesis-
dcterms.abstractIn the last decades, several blackouts have occurred due to the voltage instability and led to huge economic losses. Voltage stability becomes an increasingly concerned problem. Many methods have been developed for voltage stability analysis. However, most of the computational tools developed so far are based on predetermined set of severe but credible situations. The essential weakness of such deterministic techniques is that they do not and cannot account for the probabilistic or stochastic nature of system behavior. However, there are uncertainties such as measurement errors, forecast inaccuracy and outages of system elements in power systems. To carry out deterministic voltage stability analysis for every possible or probable combination is impractical because of an extremely large computational requirement. Therefore, the present research attempts to apply probability theory to study voltage stability problem and to improve the voltage stability of power system considering uncertainties of load forecasts and load parameters. Similar to the prevailed deterministic approaches, voltage stability will be examined via 'static' and 'dynamic' system behaviors under probabilistic environment. The static voltage stability analysis based on power flow will regard the maximum load point as the critical point, where the Jacobian matrix of power flow equation is singular. The 'dynamic' voltage stability analysis based on small disturbance and using eigenvalue analyses will consider Hopf bifurcation or saddle node bifurcation as critical point, where system state matrix has one or a pair of eigenvalues with zero real part. In deterministic studies, the degree of voltage stability is often quantified in terms of stability margin, which is the distance between the normal operating point and the critical operating point. Static voltage stability analysis based on power flow is a common tool to assess stability margin index due to its simplicity and fast calculation. Under probabilistic studies, however, system loads are random variables such that the stability margin is also random variable. In the present study, probabilistic power flow technique combined with point of collapse method will be used to obtain probabilistic characteristics of stability margin and nodal voltages at the maximum load points. Maximum entropy will be employed to determine the probabilistic distribution of stability margin according to these probabilistic characteristics.-
dcterms.accessRightsopen access-
dcterms.educationLevelPh.D.-
dcterms.extentv, 154 leaves : ill. ; 30 cm.-
dcterms.issued2008-
dcterms.LCSHHong Kong Polytechnic University -- Dissertations-
dcterms.LCSHElectric power system stability-
dcterms.LCSHProbabilities-
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