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An Improved Torque Ripple Reduction Controller for Smooth Operation of Induction Motor Drive

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Abstract

This paper describes the development of a state-of-the-art controller for induction motor drive to achieve smooth speed control under its different running conditions. The controller adopts indirect field-oriented control (IFOC) scheme for precise estimation of equivalent circuit parameters (ECPs) to generate PWM signals. The challenges for estimation of ECPs during the running condition due to change in motor temperature as well as the sudden change in its loading are dealt with a model reference adaptive system (MRAS) controller. The H–G diagram method-based reference model is utilized to estimate the reference ECPs without performing any physical tests of the motor. The backpropagation algorithm with an artificial neural network (BPANN) is utilized in its plant model while the weight and gain parameters of this model are tuned based on reference ECPs. The AdaDelta rule is utilized for fast convergence of the BPANN weights during starting conditions while stator temperature and other feedback enhance the overall performance with increased accuracy in ripple-free speed regulation. The results from MATLAB-based simulation and a hardware prototype controller using a DSPIC microcontroller with different running conditions show the efficacy of the proposed algorithm.

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Data Availability

In this work, no data have been taken from the internet. The machine specifications that have been utilized are present in the laboratory of our department.

Abbreviations

AD:

AdaDelta optimizer

AI:

Artificial intelligence

ANN:

Artificial neural network

ASD:

Adjustable speed drive

BPANN:

Backpropagation artificial neural network

CNN:

Convolution neural network

DSP:

Digital signal processing

ECPs :

Equivalent circuit parameters

EKF:

Extended Kalman’s filter

FLC:

Fuzzy logic controller

FOC:

Field-oriented control

GA:

Genetic algorithm

GD:

Gradient descent method

GNN:

Graphical neural networks

GSA:

Gravitational search algorithm

GUI:

Graphical user interface

IFOC:

Indirect field-oriented control

IM:

Induction motor

LSTM:

The long short-term memory

MMU:

Machine monitoring unit

MRAS:

Model reference adaptive structure

NN:

Neural network

PSD:

Phase-sensitive detector

PSO:

Particle swarm optimization

PT:

Physical test

PWM:

Pulse width modulation

RNN:

Recurrent neural network

V s :

Rated stator voltage

I s :

Rated stator current

I nl :

No-load current

ψ nl :

No-load flux

V an, V bn, V cn :

Instantaneous phase voltages of IM

N r :

Rated speed

P out, P rcl, P scl, P nl :

Rated power, Rotor copper loss, Stator copper-loss, No-load power

f, s :

Frequency, slip

p f , p fnl :

Power factor rated, power factor at no load

T :

Temperature at start

T e :

Electromechanical torque

v ds, v qs :

D–q axis stator voltage

v dr, v qr :

D–q axis rotor voltage

v α ,v β :

α–β Axis voltage

P :

Active power referred to the α–β axis

Q :

Reactive power referred to the α–β axis

i ds, i qs :

D–q axis stator current

i dr, i qr :

D–q axis rotor current

ψ ds, ψ qs , ψ qr , ψ dr :

D–q axis flux

I r :

Rotor current

R s :

Stator resistance

R r :

Rotor resistance

X s :

Stator reactance

X s :

Stator reactance

X r :

Rotor reactance

L s :

Stator inductance

L r :

Rotor inductance

X m :

Magnetizing reactance

t 1, t 2, t 0 :

Switching instances of SPWM

ω r :

Rotor angular speed

L m :

Magnetizing inductance

R s1 :

Stator resistance of MR

R r1 :

Rotor resistance of MR

L s1 :

Stator inductance of MR

L s2 :

Rotor inductance of MR

L m1 :

Mutual inductance of MR

Z eqR :

Per phase impedance of MR

Z eqP :

Per phase impedance of MP

Z eq :

Per phase impedance

v svm :

Voltage vector of SVM

T pwm :

The time period of PWM

M :

Modulation index

G 1 :

Controller gain of the flux flow path

G 2 :

Controller gain of the torque flow path

G 3 :

Controller gain of the slip frequency

ω s :

Synchronous speed

ω r :

Rotor speed

ω sl :

Slip speed

τ R :

Rotor time constant

M R , M P :

Reference and plant model

h :

Hidden layer

S :

Activation function

η :

Learning rate

g n :

Gradient

r n :

Summation of the squared term of gradients

w d :

Window size

w (n + 1) :

Modified weight after nth iterations

w n :

Current weight of nth iterations

y :

The output of the forward path

Y MR , Y MP :

Output ECP for reference and plant model

b, e :

Loss function and error

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Acknowledgements

The authors are thankful to Mr. Rakesh Das research scholar of the Applied Physics department of CU for helping us to develop the MMU.

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Authors and Affiliations

  1. Department of Applied Physics, University of Calcutta, Kolkata, India

    Tista Banerjee & Jitendra Nath Bera

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  1. Tista Banerjee
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This work has been prepared entirely by TB under the supervision of JNB.

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Correspondence to Tista Banerjee.

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Banerjee, T., Bera, J.N. An Improved Torque Ripple Reduction Controller for Smooth Operation of Induction Motor Drive. J Control Autom Electr Syst 34, 247–264 (2023). https://doi.org/10.1007/s40313-022-00945-8

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