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Artificial neural networks for state estimation of electric power systems
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2014
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| _version_ | 1867613225295544320 |
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| access_status_str | Open Access |
| author | Zivanovic, Rastko |
| author2 | Petroianu, Alexander |
| author_browse | Petroianu, Alexander Zivanovic, Rastko |
| author_facet | Petroianu, Alexander Zivanovic, Rastko |
| author_sort | Zivanovic, Rastko |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/9470 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:45.765Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/9470 Artificial neural networks for state estimation of electric power systems Zivanovic, Rastko Petroianu, Alexander Electrical Engineering Includes bibliographical references. 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. 2014-11-10T08:54:46Z 2014-11-10T08:54:46Z 1996 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/9470 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Zivanovic, Rastko Artificial neural networks for state estimation of electric power systems |
| thesis_degree_str | Doctoral |
| title | Artificial neural networks for state estimation of electric power systems |
| title_full | Artificial neural networks for state estimation of electric power systems |
| title_fullStr | Artificial neural networks for state estimation of electric power systems |
| title_full_unstemmed | Artificial neural networks for state estimation of electric power systems |
| title_short | Artificial neural networks for state estimation of electric power systems |
| title_sort | artificial neural networks for state estimation of electric power systems |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/9470 |
| work_keys_str_mv | AT zivanovicrastko artificialneuralnetworksforstateestimationofelectricpowersystems |