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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 32, Number 6(1) (2020)
Copyright(C) MYU K.K.
pp. 1955-1967
S&M2232 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2785
Published: June 10, 2020

Direct Torque Control Induction Motor Drive for Speed Estimation Using Modified Particle Swarm Optimization Algorithm [PDF]

Yung-Chang Luo, Xu-Hong Zheng, Chien-Hua Liao, and Ying-Piao Kuo

(Received December 19, 2019; Accepted April 22, 2020)

Keywords: speed estimation, direct torque control (DTC), particle swarm optimization (PSO) algorithm, flux observer, voltage space vector pulse width modulation (VSVPWM)

In this study, the current and flux linkage of a stator were used to develop a speed estimation direct torque control induction motor (IM) drive, and the speed estimation signal was derived from the stator flux observer. Hall effect current sensors were used to obtain the stator current from the IM. Voltage space vector pulse width modulation was used to establish a voltage source inverter (VSI) in place of a traditional switching table VSI to reduce the ripples of both the stator current and electromagnetic torque. The Lyapunov stability theory was used to design the stator flux observer, and the gain matrix of this observer was acquired using the modified particle swarm optimization algorithm. The operation speed was extended to greater than the base speed using field weakening. The Matlab/Simulink© toolbox was used to develop this simulation system and all the control algorithms were realized using a Texas Instruments DSP 6713-and-F2812 control card to generate pulse width modulation signals for the power stage to the actuate IM and validate this approach. Both simulation and experimental results confirmed the effectiveness of the proposed system.

Corresponding author: Yung-Chang Luo


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Cite this article
Yung-Chang Luo, Xu-Hong Zheng, Chien-Hua Liao, and Ying-Piao Kuo, Direct Torque Control Induction Motor Drive for Speed Estimation Using Modified Particle Swarm Optimization Algorithm, Sens. Mater., Vol. 32, No. 6, 2020, p. 1955-1967.



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