AC–DC converter switches of the drive train of series hybrid electric vehicles (SHEVs) are generally exposed to the possibility of outbreak open-phase faults because of troubles with the switching devices. In this framework, the present study proposes an artificial neural network (ANN)-based method for fault diagnosis after extraction of a new pattern. The new pattern under AC–DC converter failure in view of SHEV application has been used for train-proposed ANN. To achieve this goal, four different levels of switches fault are considered on the basis of both simulation and experimental results. Ensuring the accuracy and generalisation of the introduced pattern, several parameters have been considered, namely: capacitor size changes, load, and speed variations. The experimental results validate the simulation results thoroughly.
Artificial neural network-based fault diagnosis in the AC–DC converter of the power supply of series hybrid electric vehicle
IET Electrical Systems in Transportation ; 6 , 2 ; 96-106
01.06.2016
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
velocity control , load variation , train-proposed ANN , outbreak open-phase faults , series hybrid electric vehicle , artificial neural network-based fault diagnosis , fault diagnosis , capacitor switching , AC-DC converter failure , neural nets , switching devices , power supply , AC-DC power convertors , capacitor size changes , SHEV , hybrid electric vehicles , power engineering computing , speed variation , control engineering computing , load regulation , AC-DC converter switches
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