Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/30771
Title: | Fault detection in photovoltaic power converter |
Authors: | KAFI, Mohamed Redouane KAFI, Omar Rafik ABDESSAMED, Zakaria |
Keywords: | Faultdiagnosisbasedmachinelearning multicellularpowerconverter photovoltaicsystem nolinearcontrol |
Issue Date: | 2022 |
Abstract: | This workdealswithfaultdiagnosisofpowerconverterusedinphotovoltaicsystems whichsupplyandisolatedelectricloadandhowittransformthecurrentsafelytodevices. this thesistreatthemulticellularpowerconverterdescribehowmuchthepowerconverter failures influencetotheloadcurrentthemainfocusingisinthecapacitorsfaultswhich can affectbadlytotheloadcurrentssoitcanresultbadconsequencesondevices.which proposedasolutionforthisproblemusing(KNN)k-nearestneighbormachinelearning algorithm tobuildaclassificationmodelfordiagnosis.andusingtwomodesofcontrolin order tocompareintermofthefunctionunderfailures,loadcurrentpreservationand the smoothest,andintermoftheaccuracyoftheclassificationmodelbuilt.aswellas to usingtheslidingmodecontrolmodeandtheexactlinearizationmode,thisisforthe purposeofcomparisonintermsofsystemperformanceduringtheoccurrenceoffaults and theextentoftheirimpactontheloadcurrentbyexaminingtheshapeofitssignal and analyzingtherobustnessofthetwocontrolsinnotbeinggreatlyaffectedbythe faults andshowingit. |
Description: | Automatic and systems |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/30771 |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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KAFI Omar Rafik ABDESSAMED Zakaria.pdf | Automatic and systems | 3,42 MB | Adobe PDF | View/Open |
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