Artificial neural networks for state estimation of electric power systems

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dc.contributor.advisorPetroianu, Alexanderen_ZA
dc.contributor.authorZivanovic, Rastkoen_ZA
dc.date.accessioned2014-11-10T08:54:46Z
dc.date.available2014-11-10T08:54:46Z
dc.date.issued1996en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThis thesis deals with the application of Artificial Neural Network (ANN) technology in power system state estimation. It addresses the following developments: the fundamentals of the state estimation based on ANN technology; the feasible ANN state estimation methods; use of voltage phasor angle measurements in ANN state estimation; and bad data processing for ANN state estimation. The power system state estimation problem is formulated as an optimization problem applied to dynamic ANN model. Two types of dynamic ANN models are used: ANN model with steepest descent dynamic; and ANN model with Hopfield-style dynamic. The complexity of an ANN State Estimator (ANN SE) is reduced by using the following techniques: a special algebraic transformation of the ANN objective function; and the incorporation of zero-injection measurements by the using variable reduction technique. At the same time, these two techniques improve the filtering performance of the ANN SE. Two methods for designing the ANN SE for a specific power system are developed: an analytical method: it maps the structure and the parameters of a power system into the ANN SE structure and parameters; and a synthetic method: it is based on the Real Time Recurrent Learning (RTRL) technique (used in training dynamic ANN), where the ANN SE structure and parameters are determined through learning from available input/output (measurements/state variables) data. In continuation of the thesis feasible ANN SE methods are developed.en_ZA
dc.identifier.apacitationZivanovic, R. (1996). <i>Artificial neural networks for state estimation of electric power systems</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/9470en_ZA
dc.identifier.chicagocitationZivanovic, Rastko. <i>"Artificial neural networks for state estimation of electric power systems."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996. http://hdl.handle.net/11427/9470en_ZA
dc.identifier.citationZivanovic, R. 1996. Artificial neural networks for state estimation of electric power systems. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Zivanovic, Rastko AB - This thesis deals with the application of Artificial Neural Network (ANN) technology in power system state estimation. It addresses the following developments: the fundamentals of the state estimation based on ANN technology; the feasible ANN state estimation methods; use of voltage phasor angle measurements in ANN state estimation; and bad data processing for ANN state estimation. The power system state estimation problem is formulated as an optimization problem applied to dynamic ANN model. Two types of dynamic ANN models are used: ANN model with steepest descent dynamic; and ANN model with Hopfield-style dynamic. The complexity of an ANN State Estimator (ANN SE) is reduced by using the following techniques: a special algebraic transformation of the ANN objective function; and the incorporation of zero-injection measurements by the using variable reduction technique. At the same time, these two techniques improve the filtering performance of the ANN SE. Two methods for designing the ANN SE for a specific power system are developed: an analytical method: it maps the structure and the parameters of a power system into the ANN SE structure and parameters; and a synthetic method: it is based on the Real Time Recurrent Learning (RTRL) technique (used in training dynamic ANN), where the ANN SE structure and parameters are determined through learning from available input/output (measurements/state variables) data. In continuation of the thesis feasible ANN SE methods are developed. DA - 1996 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1996 T1 - Artificial neural networks for state estimation of electric power systems TI - Artificial neural networks for state estimation of electric power systems UR - http://hdl.handle.net/11427/9470 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9470
dc.identifier.vancouvercitationZivanovic R. Artificial neural networks for state estimation of electric power systems. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9470en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleArtificial neural networks for state estimation of electric power systemsen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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