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Health management using fault detection and fault tolerant control of multicellular converter applied in more electric aircraft system

Mahboub Mohamed Abdelbasset, Rouabah Boubakeur, Kafi Mohamed Redouane, Toubakh Houari

Diagnostyka

2022

Vol. 23, No. 2

art. no. 2022212

The increased cost of fuel and maintenance in aircraft system lead to the concept of more electric aircraft, moreover this concept increase the use of power electronic converters in aircraft power system. Since in this application, the reliability is a crucial feature. Therefore, the use of more efficient, reliable and robust power converter with health management capability will be a big challenge. Multicellular topology of power converters has the required performance in terms of efficiency and robustness. However, the increased complexity of control and more power components (power switches and capacitors) goes along with an increase in possibility of failure in multicellular topology. Therefore, the main contribution of this paper is the use of multicellular topology advantageous with fault diagnosis and fault tolerant control in order to increase the robustness reliability. The health management using a fault detection with Fuzzy Pattern Matching (FPM) algorithm when a failure in power switches or flying capacitors of multicellular converter and a Fault Tolerant Control (FTC) with sliding mode of second parallel three cells multicellular converters. Simulation results with Matlab show the increased efficiency and the continuity of work during failure mode in aircraft power system.

Smart control based on neural networks for multicellular converters

Laidi Kamel, Bouchhida Ouahid, Nibouche Mokhtar, Benmansour Khelifa

Archives of Electrical Engineering

2021

Vol. 70, nr 3

531--550

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.