Implementación de técnica de Machine Learning para ubicación de fallas en sistemas de potencia
"Fault location algorithms are classified into four main categories: one-ended techniques, two terminal methods, traveling-wave techniques and time-tagged methods. Algorithms that belong to the first one are usually based on impedance calculations and, consequently, are affected by diverse erro...
- Autores:
-
Bohórquez Muñoz, Juan Pablo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2017
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/61184
- Acceso en línea:
- http://hdl.handle.net/1992/61184
- Palabra clave:
- Aprendizaje automático (Inteligencia artificial)
Localización de fallas eléctricas
Protección de sistemas de energía eléctrica
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | "Fault location algorithms are classified into four main categories: one-ended techniques, two terminal methods, traveling-wave techniques and time-tagged methods. Algorithms that belong to the first one are usually based on impedance calculations and, consequently, are affected by diverse error sources such as fault impedance, inaccurate fault type identification, line compensation, line parameters uncertainty, among others. Moreover, increasing algorithms precision may decrease the fault correction time and, therefore, implies positive economic outcomes".-- Tomado del Formato de Documento de Grado. |
|---|