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https://dspace.univ-ouargla.dz/jspui/handle/123456789/30863
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | KAFI M., Redouane | - |
dc.contributor.author | BOUMADDA, Kenza | - |
dc.contributor.author | BOURENANE, Nesrine | - |
dc.date.accessioned | 2022-10-11T13:31:01Z | - |
dc.date.available | 2022-10-11T13:31:01Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | https://dspace.univ-ouargla.dz/jspui/handle/123456789/30863 | - |
dc.description | Network Administration and Security | en_US |
dc.description.abstract | Nowadays, Artificial intelligence applications have increased importance in renewable energies, such as photovoltaic systems, especially in data analysis and fault detection. Therefore, in this paper, a standalone solar photovoltaic system based on multicellular converter with flying capacitor fault detection using machine learning algorithms. Two control strategies; sliding mode and exact linearization controls are used in this paper in order to determine the more robustness and increased accuracy control. Simulation results with MATLAB using K-Nearest Neighbors (KNN) algorithm show that sliding mode control present high accuracy and improved robustness compared with exact linearization control. | en_US |
dc.language.iso | en | en_US |
dc.publisher | UNIVERSITY OF KASDI MERBAH OUARGLA | en_US |
dc.subject | Photovoltaic systems | en_US |
dc.subject | power converter | en_US |
dc.subject | KNN | en_US |
dc.subject | sliding mode | en_US |
dc.subject | exact-linearization mode | en_US |
dc.subject | fault detection | en_US |
dc.title | Fault detection in photovoltaic power converter using machine learning algorithm | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département d'informatique et technologie de l'information - Master |
Files in This Item:
File | Description | Size | Format | |
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BOUMADDA _ BOURENANE .pdf | Network Administration and Security | 2,07 MB | Adobe PDF | View/Open |
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