Stator Resistance Estimation Of Modified DTC IM Drives Using Fuzzy Logic

Naveen Goel*, Ram Narayan Patel**, Saji Chacko***
* Research Scholar, Swami Vivekanand Technical University, Bhilai (C.G.), India.
** Professor, Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, India.
*** HOD, Department of Electrical, Government Polytechnic College, C.G, India.

Abstract

In electric drives used in industries are adjustable speed drives and AC Induction motors are used in most of these drives.
Due to their low cost, reliability and performance, they find application in a variety of domains. A number of techniques
are reported in the literature for control of induction motor drive. Amongst them, the most advanced and popular
technology is the Direct Torque Control (DTC) drive. The control strategy of DTC Induction motor drive requires estimation
of torque and flux. The estimation of flux depends on stator resistance (R ). During drive operation, the motor temperature S
increases. With increase in motor temperature the R increases and it is found that the change in R is normally in between S S
the 0.75 to 1.7 times to its normal value. This leads to erroneous flux estimation leading to high flux and torque ripple.
Therefore, an online estimation of R is required for robust performance of DTC drive. In this paper, the estimation of R is S S
based on the variation of stator current is proposed. The estimation is based on model reference adaptive system with
the adaptive mechanism using the Proportional Integral (PI) and Fuzzy Logic Controller (FLC). The performance of the
DTC drive using the two different adaptive mechanisms is compared and results obtained show improved performance
in terms of motor speed, torque and flux developed despite changes in R .

Keywords

Low Pass Filter, Fuzzy Logic, Direct Torque Control, Induction Motor, PI Controller.

How to Cite this Article?

Goel, N., Patel, R. N., and Chacko, S. (2018). Stator Resistance Estimation Of Modified DTC IM Drives Using Fuzzy Logic. i-manager’s Journal on Electrical Engineering, 11(3), 34-44.

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