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Salah, Samer Z.
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Computer Science & IT Electrical & Electronics Engineering

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High-performance speed control for three-phase induction motor based on reverse direction algorithm and artificial neural network Al-Khawaldeh, Mustafa A.; Ghaeb, Jasim A.; Salah, Samer Z.; Alrawajfeh, Mohammad S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6237-6247

Abstract

This research proposes two approaches for determining the required frequency and modulation index for a pulse-width-modulation (PWM) system in a variable frequency drive (VFD) to control the speed of the three-phase induction motor. The first approach which is the reverse direction algorithm (RDA), uses a set of equations to calculate the necessary frequency and voltage for maintaining a constant motor speed under varying load conditions. The second one involves training a neural network (NN) on data collected by the RDA, which can then be used to continuously adjust the motor speed in real time to adapt to changing load torque requirements. Simulation and laboratory models for the three-phase induction motor are built and the proposed RDA-NN controller is examined. Results have proved that the proposed controller is effective in providing a stable and responsive motor speed control system.
Developing an algorithm for the adaptive neural network for direct online speed control of the three-phase induction motor Al-Mahasneh, Ahmad J.; Salah, Samer Z.; Ghaeb, Jasim A.; Baniyounis, Mohammed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1499-1510

Abstract

In this paper, an online adaptive general regression neural network (OAGRNN) is presented as a direct online speed controller for a three-phase induction motor. To keep the induction motor running at its rated speed in real-time and under a variety of load conditions, the speed error and its derivative are continuously measured and fed back to the OAGRNN controller. The OAGRNN controller provides the inverter with the control signal it needs to produce the proper frequency and voltage for the induction motor instantly. Notably, the OAGRNN controller demonstrated remarkable performance without the need for a learning mode; it was able to track the desired motor speed, starting its operation from scratch. A setup utilizing a three-phase induction motor has been developed to show the high capacity of OAGRNN for tracking the desired speed of the motor while subjected to the varied load torque. The performance of OAGRNN is examined in two phases: the MATLAB simulation and the experimental setup. Furthermore, when the OAGRNN performance is compared with that of the proportional integral (PI) controller, it demonstrates its outstanding ability and superiority for online adjustments related to the three-phase induction motor's speed control.
Co-Authors Al-Khawaldeh, Mustafa A. Al-Mahasneh, Ahmad J. Alrawajfeh, Mohammad S. Baniyounis, Mohammed Ghaeb, Jasim A.