Smart control based on neural networks for multicellular converters

• Autorzy: Laidi Kamel , Bouchhida Ouahid , Nibouche Mokhtar , Benmansour Khelifa

Identyfikatory

DOI

10.24425/aee.2021.137572

• Języki publikacji: EN

Abstrakty

EN

A smart control based on neural networks for multicellular converters has been developed and implemented. The approach is based on a behavioral description of the different converter operating modes. Each operating mode represents a well-defined configuration for which an operating zone satisfying given invariance conditions, depending on the capacitors’ voltages and the load current of the converter, is assigned. A control vector, whose components are the control signals to be applied to the converter switches is generated for each mode. Therefore, generating the control signals becomes a classification task of the different operating zones. For this purpose, a neural approach has been developed and implemented to control a 2-cell converter then extended to a 3-cell converter. The developed approach has been compared to super-twisting sliding mode algorithm. The obtained results demonstrate the approach effectiveness to provide an efficient and robust control of the load current and ensure the balancing of the capacitors voltages.

Słowa kluczowe

EN

multicellular converters; neural networks; smart control

PL

konwertery wielokomórkowe; sieci neuronowe; inteligentne sterowanie

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2021
• Tom: Vol. 70, nr 3
• Strony: 531--550
• Opis fizyczny: Bibliogr. 18 poz., rys., wz.

Twórcy

autor

Laidi Kamel

• University of Medea, Algeria

autor

Bouchhida Ouahid

• University of Medea, Algeria

autor

Nibouche Mokhtar

• University of the West of England, United Kingdom

autor

Benmansour Khelifa

• University of Medea, Algeria

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).