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Sensors and Materials
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Speed Estimation of Adaptive Stator-flux-vector-controlled Induction Motor Drive Based on Particle Swarm Optimization Algorithm

Yung-Chang Luo, Sheng-Hong Pei, Ying-Piao Kuo, and Cheng-Tao Tsai

(Received May 9, 2019; Accepted February 10, 2020)

Keywords: speed estimation, stator flux vector control (SFVC), model reference adaptive system (MRAS), particle swarm optimization (PSO) algorithm, field weakening

An adaptive synchronous speed estimation scheme is proposed for speed estimation of a stator-flux-vector-controlled (SFVC) induction motor (IM) drive. An SFVC IM drive was established according to the current and flux of the stator, and the stator current was obtained from an IM by using Hall effect current sensors. A model reference adaptive system (MRAS) was utilized to design the synchronous speed identification scheme based on the reactive power, and the estimated rotor speed was obtained by subtracting the slip speed from the estimated synchronous speed. The adaptation mechanism of the MRAS was designed using the particle swarm optimization (PSO) algorithm. The available operation speed was extended to the constant-power mode by applying the field-weakening technique. The MATLAB\Simulink® toolbox was used to simulate this system, and all the control algorithms were realized using a Texas Instruments 6713-and-F2812 DSP card to generate pulse-width modulation signals for the power stage to actuate the IM. Both the simulation and experiment results (including the estimated rotor speed, stator current, electromagnetic torque, and stator flux locus) confirm the effectiveness of the proposed system and validate the proposed approach.

Corresponding author: Yung-Chang Luo




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