Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques
This is a project which consisted on fault and anomaly detection for photovoltaic systems. It started with a study of models that could detect the outliers for data, then an electrical fault and anomaly labeling was necessary and finally, a classification of these labels was made in order to make co...
- Autores:
-
Pardo Morales, Santiago Iván
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/73830
- Acceso en línea:
- https://hdl.handle.net/1992/73830
- Palabra clave:
- Machine learning
Photovoltaic system
Data analysis
Fault
Anomaly
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
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| dc.title.eng.fl_str_mv |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| dc.title.alternative.spa.fl_str_mv |
Detección y clasificación de fallas en un sistema fotovoltaico de una planta de 80 kW usando técnicas de aprendizaje automático |
| title |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| spellingShingle |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques Machine learning Photovoltaic system Data analysis Fault Anomaly Ingeniería |
| title_short |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| title_full |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| title_fullStr |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| title_full_unstemmed |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| title_sort |
Fault detection and classification in a 80kW photovoltaic system using Machine Learning techniques |
| dc.creator.fl_str_mv |
Pardo Morales, Santiago Iván |
| dc.contributor.advisor.none.fl_str_mv |
Jiménez Estévez, Guillermo Andrés Bressan, Michael Giraldo Trujillo, Luis Felipe |
| dc.contributor.author.none.fl_str_mv |
Pardo Morales, Santiago Iván |
| dc.contributor.jury.none.fl_str_mv |
López Jiménez, Jorge Alfredo |
| dc.subject.keyword.eng.fl_str_mv |
Machine learning |
| topic |
Machine learning Photovoltaic system Data analysis Fault Anomaly Ingeniería |
| dc.subject.keyword.none.fl_str_mv |
Photovoltaic system Data analysis Fault Anomaly |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
This is a project which consisted on fault and anomaly detection for photovoltaic systems. It started with a study of models that could detect the outliers for data, then an electrical fault and anomaly labeling was necessary and finally, a classification of these labels was made in order to make conclusions about the performance of the models. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-02-02T18:59:53Z |
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2024-02-02T18:59:53Z |
| dc.date.issued.none.fl_str_mv |
2024-01-12 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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https://hdl.handle.net/1992/73830 |
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https://hdl.handle.net/1992/73830 |
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instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
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eng |
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eng |
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Attribution 4.0 International |
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http://creativecommons.org/licenses/by/4.0/ |
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info:eu-repo/semantics/openAccess |
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openAccess |
| dc.format.extent.none.fl_str_mv |
54 páginas |
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application/pdf |
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Universidad de los Andes |
| dc.publisher.program.none.fl_str_mv |
Ingeniería Electrónica |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingeniería |
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Departamento de Ingeniería Eléctrica y Electrónica |
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Universidad de los Andes |
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Universidad de los Andes |
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