Smart Fault Detection in Nanosatellite Electrical Power System
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915703753277440 |
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| author | Rezaee, Alireza Nobahari, Niloofar Asgarifar, Amin Hajati, Farshid |
| author_facet | Rezaee, Alireza Nobahari, Niloofar Asgarifar, Amin Hajati, Farshid |
| contents | This paper presents a new detection method of faults at Nanosatellites' electrical power without an Attitude Determination Control Subsystem (ADCS) at the LEO orbit. Each part of this system is at risk of fault due to pressure tolerance, launcher pressure, and environmental circumstances. Common faults are line to line fault and open circuit for the photovoltaic subsystem, short circuit and open circuit IGBT at DC to DC converter, and regulator fault of the ground battery. The system is simulated without fault based on a neural network using solar radiation and solar panel's surface temperature as input data and current and load as outputs. Finally, using the neural network classifier, different faults are diagnosed by pattern and type of fault. For fault classification, other machine learning methods are also used, such as PCA classification, decision tree, and KNN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00335 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Smart Fault Detection in Nanosatellite Electrical Power System Rezaee, Alireza Nobahari, Niloofar Asgarifar, Amin Hajati, Farshid Machine Learning This paper presents a new detection method of faults at Nanosatellites' electrical power without an Attitude Determination Control Subsystem (ADCS) at the LEO orbit. Each part of this system is at risk of fault due to pressure tolerance, launcher pressure, and environmental circumstances. Common faults are line to line fault and open circuit for the photovoltaic subsystem, short circuit and open circuit IGBT at DC to DC converter, and regulator fault of the ground battery. The system is simulated without fault based on a neural network using solar radiation and solar panel's surface temperature as input data and current and load as outputs. Finally, using the neural network classifier, different faults are diagnosed by pattern and type of fault. For fault classification, other machine learning methods are also used, such as PCA classification, decision tree, and KNN. |
| title | Smart Fault Detection in Nanosatellite Electrical Power System |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.00335 |