Impact of sensorless NDTC in a fuel cell traction system

Benhamou Issa, Tedjini Hamza, Guettaf yacine, Nour Mohamed


Due to the reliability and relatively low cost and modest maintenance requirement of the induction machine make it one of the most widely used machines in industrial applications. The speed control is one of many problems in the traction system, researchers went to new paths instead the classical controllers as PI controller, they integrated the artificial intelligent for its yield. The clasical DTC is a method of speed control by using speed sensorand PI controler, it achieves a decoupled control of the electromagnetic torque and the stator flux in the stationary frame, besides, the use of speed sensors has several drawbacks such as the fragility and the high cost, for this reason, the specialists went to propose an estimators as kalman filter. The fuel cell is a new renewable energy, it has many application in the traction systems as train, bus. This paper presents an improved control using DTC by integrate the neural network strategy without use speed sensor (Sensorless Control) to reduce overtaking and current ripple and static errorin the sytem because the PI controller has some problems like this; and reduce the cost with use a renewable energy as fuel cell.


fuel cells; induction motor (IM), kalman filter; neural network direct torque control (NDTC); sensorless;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578