Fault diagnosis of power electronic converter

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DC Field	Value	Language
dc.contributor.author	ROUABAH, Boubakeur	-
dc.contributor.author	AD, Hana	-
dc.contributor.author	SERKOU, Arwa Oum elbaha	-
dc.date.accessioned	2024-10-06T15:26:05Z	-
dc.date.available	2024-10-06T15:26:05Z	-
dc.date.issued	2024	-
dc.identifier.citation	FACULTY OF NEW TECHNOLOGIES OF INFORMATION AND COMMUNICATION	en_US
dc.identifier.uri	https://dspace.univ-ouargla.dz/jspui/handle/123456789/37123	-
dc.description	Instrumentation and System	en_US
dc.description.abstract	Modular Multilevel Converter MMCs are an advanced power electronics topology that has many advantages, including high efficiency, scalability, and superior harmonic performance. However, as with any complex system, MMC devices are susceptible ti errors that can affect their operation and reliability. Machine learning is one of the most important technologies to enhance fault diagnosis in MMC, enabling rapid identification and remediation of problems to reliability of power conversion systems.	en_US
dc.description.sponsorship	Department of Electronics and Telecommunications	en_US
dc.language.iso	en	en_US
dc.publisher	UNIVERSITY OF KASDI MERBAH OUARGLA	en_US
dc.subject	Modular Multilevel Converter	en_US
dc.subject	Machine learning	en_US
dc.subject	power conversion systems	en_US
dc.title	Fault diagnosis of power electronic converter	en_US
dc.type	Thesis	en_US
Appears in Collections:	Département d'Electronique et des Télécommunications - Master

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