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DTC-IM drive using adaptive neuro fuzzy inference strategy with multilevel inverter

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Abstract

This paper presents the speed control of direct torque controlled 3ɸ induction motor using adaptive neuro-fuzzy inference strategy (ANFIS). ANFIS controller has been utilized to produce a reference signal for the SVPWM. The gate pulses for the 3ɸ voltage source inverter (VSI) have been obtained from SVPWM. The VSI has finally controlled the induction motor. The Simulink model for this work has been created in MATLAB. The performance exploration of the DTC-IM drive system using ANFIS has been considered, trained, and accomplished in this paper. Simulations have been done for different speeds such as 800, 1000, 1200, and 1400 rpm for both conventional and five-level inverter. The simulation results have revealed that dynamic along with a transient performance of the drive has been improved using ANFIS control strategy. During the sudden variation in load torque, the machine gives good stabilization with admirable learning capability of neural networks by the use of the ANFIS controller. Moreover, the proposed five-level inverter minimizes the total harmonic distortion (THD) in the current and voltage of the inverter compared to the conventional two-level inverter. The same model has been implemented in an experimental prototype to check the feasibility of the proposed configuration.

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Abbreviations

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

DSC:

Direct self control

DTC:

Direct torque control

DSVM:

Discrete space vector modulation

FLC:

Fuzzy logic control

e(k):

Speed control error

FOC:

Field oriented control

J:

Moment of inertia

Te :

Instantaneous value of electromagnetic torque

LSPMSM:

Line-start permanent magnet synchronous motor

MPPT:

Maximum power point tracking

P:

No. of pole pairs

PID:

Proportional integral derivative

PTC:

Predictive torque control

PI:

Proportional integral

PWM:

Pulse width modulation

θs , θr:

Stator and rotor angle

Ls, Lr :

Stator and rotor inductance

Lm :

Mutual inductance

Lls, Llr :

Stator and rotor leakage inductance

Rs, Rr :

Stator and rotor resistance

TL :

Load torque

Ns :

Number of switching states

NV :

Number of space vectors

NT :

Number of triangles

SCR:

Silicon controlled rectifier

SVM:

Space vector modulation

SVPWM:

Space vector pulse width modulation

Ψds , Ψdr :

D-axis stator and rotor flux linkage

Ψqs , Ψqr :

Q-axis stator and rotor flux linkage

Ψqm , Ψdm :

Q-axis and d-axis mutual flux linkage

ids, idr :

D-axis stator and rotor current

iqs, iqr :

Q-axis stator and rotor current

vds, vdr :

D-axis stator and rotor voltage

Vqs, vqr :

Q-axis stator and rotor voltage

vs, is :

Stator voltage and current

vr, ir :

Rotor voltage and current

ω:

Angular velocity

ωre f :

Speed reference

ωr :

Rotor speed

θm :

Stator to rotor angle

THD:

Total harmonic distortion

VSI:

Voltage source inverter

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Mahath Amma Institute of Engineering and Technology, Pudukkottai, India

    J. Barsana Banu

  2. Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Virudhunagar, Tamil Nadu, India

    J. Jeyashanthi

  3. Petroleum Research Center, Kuwait Institute of Scientific Research, Safat, Kuwait

    A. Thameem Ansari

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  1. J. Barsana Banu
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  2. J. Jeyashanthi
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  3. A. Thameem Ansari
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Correspondence to J. Barsana Banu.

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Banu, J.B., Jeyashanthi, J. & Ansari, A.T. DTC-IM drive using adaptive neuro fuzzy inference strategy with multilevel inverter. J Ambient Intell Human Comput 13, 4799–4821 (2022). https://doi.org/10.1007/s12652-021-03244-3

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