Algoritmo de localización de fallas en sistemas de distribución basado en machine learning
This work presents a fault location method in distribution systems based on neuronal networks using a phase-angle jump as the model?s single input. The IEEE 34 nodes system was used. Different fault scenarios have been considered to train ANN models including various incipient angles, fault types, f...
- Autores:
-
Gordillo Sierra, Juan David
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51511
- Acceso en línea:
- http://hdl.handle.net/1992/51511
- Palabra clave:
- Sistemas de energía eléctrica
Distribución de energía eléctrica
Fallas en la energía eléctrica
Localización de fallas eléctricas
Redes eléctricas inteligentes
Aprendizaje automático (Inteligencia artificial)
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | This work presents a fault location method in distribution systems based on neuronal networks using a phase-angle jump as the model?s single input. The IEEE 34 nodes system was used. Different fault scenarios have been considered to train ANN models including various incipient angles, fault types, fault resistance values, and various fault distances that typically affect a fault location algorithm?s accuracy. Different load conditions were not considered in this particular study. Nine different models were trained specifically with particular fault types and fault resistance values and one model was trained with all fault scenarios regardless of the latter obtaining the best performance with three different models trained specifically for locating each fault type considered. |
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