Recursive subspace identification for fault detection
Future electrical grids will require new ways to identify faults, as inverters are not capable of supplying large fault currents to support existing fault detection methods, and because distributed resources may feed faults from the edge of the grid. This master’s thesis proposes using online, subsp...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/428836 |
| Acceso en línea: | https://hdl.handle.net/2117/428836 |
| Access Level: | acceso abierto |
| Palabra clave: | MIMO systems Electric power systems System analysis Sistemes MIMO Sistemes de distribució d'energia elèctrica Anàlisi de sistemes Àrees temàtiques de la UPC::Enginyeria elèctrica::Distribució d’energia elèctrica::Xarxes elèctriques |
| Sumario: | Future electrical grids will require new ways to identify faults, as inverters are not capable of supplying large fault currents to support existing fault detection methods, and because distributed resources may feed faults from the edge of the grid. This master’s thesis proposes using online, subspace/time-domain system identification for online power system fault detection. This innovative approach can detect high impedance faults, thus improving the reliability and safety of modern electrical grids. State-Space and ARX model methods are compared, demonstrating that ARX model methods are better suited for the task. Furthermore, a recursive ARX method is proposed which increases the detection speed. A discussion of the theoretical foundations, as well as practical implementations, is presented with simulation results validating the effectiveness and robustness of the proposed methods. |
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