Anglia Ruskin Research Online (ARRO)
Browse
An Improved FPGA Implementation of Direct Torque Control for Induction Machines.pdf (1.11 MB)

An improved FPGA implementation of direct torque control for induction machines

Download (1.11 MB)
journal contribution
posted on 2023-08-30, 13:51 authored by Tole Sutikno, Nik R. N. Idris, Auzani Jidin, Marcian N. Cirstea
This paper presents a novel direct torque control (DTC) approach for induction machines, based on an improved torque and stator flux estimator and its implementation using Field Programmable Gate Arrays (FPGA). The DTC performance is significantly improved by the use of FPGA, which can execute the DTC algorithm at higher sampling frequency. This leads to the reduction of the torque ripple and improved flux and torque estimations. The main achievements are: i) calculating a discrete integration operation of stator flux using backward Euler approach, ii) modifying a so called non-restoring method in calculating the complicated square root operation in stator flux estimator, iii) introducing a new flux sector determination method, iv) increasing the sampling frequency to 200kHz such that the digital computation will perform similar to that of the analog operation, and v) using two’s complement fixed-point format approach to minimize calculation errors and the hardware resource usage in all operations. The design was achieved in VHDL, based on a Matlab/Simulink simulation model. The Hardware-In-the-Loop (HIL) method is used to verify the functionality of the FPGA estimator. The simulation results are validated experimentally. Thus, it is demonstrated that FPGA implementation of DTC drives can achieve excellent performance at high sampling frequency.

History

Refereed

  • Yes

Volume

9

Issue number

3

Page range

1280-1290

Publication title

IEEE Transactions on Industrial Informatics

ISSN

1941-0050

Publisher

IEEE

File version

  • Accepted version

Language

  • other

Legacy posted date

2013-07-22

Legacy creation date

2022-01-26

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC