This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented. With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.

Indirect field oriented vector-controlled
induction motor drives are widely used in industrial applications for
high-performance drive systems. Because indirect field orientation utilizes an
inherent slip relation, it is essentially a feedforward scheme and hence
naturally parameter sensitive, particularly to the rotor resistance. A mismatch
between the actual rotor flux and the estimated rotor flux leads to error
between the actual motor torque and the estimated torque and hence leads to poor
dynamic performance. The accuracy of the estimated rotor flux is greatly influenced
by the value of rotor resistance (

Several
methods have been reported to minimize the consequences of parameter sensitivity
in indirect vector-controlled drives. The methods discussed in [

In
this paper, online estimators are developed to address the situation of similar
disturbances in both stator and rotor resistance simultaneously. Section

Parameter identification using neural networks.

The
rotor and stator resistance estimators described in Sections

The basic structure of an adaptive scheme described by Figure

Structure of the neural network system for

The current model (

The sample data model of
(

The neural
network model represented by (

Two-layered neural network model.

The weights of
the network,

To accelerate
the convergence of the error back propagation learning algorithm, the current
weight adjustments are
supplemented with a fraction of the most recent weight adjustment, as

Similarly,
the changes in

The
rotor resistance

The rotor resistance estimator described in this section, has used the
fluxes

The voltage and current model equations of the induction
motor, (

The weights

To examine the
effect of stator resistance variation in the amplitude of stator current,
modeling studies were carried out with a ramp change in stator resistance. The
stator current profile is shown in Figure

Relationship between

Equation (

The weight
adjustment for

To accelerate
the convergence of the error back propagation learning algorithm, the current
weight adjustments are
supplemented with a fraction of the most recent weight adjustment, as in

Similarly, using the discrete
form of (

Equation (

The weight

The stator resistance

The stator resistance of an induction motor can be thus
estimated from the stator current using the neural network system as indicated
in Figure

The block
diagram of a rotor flux oriented induction motor drive, together with both
stator and rotor resistance identifications, is shown in Figure

Schematic of the indirect vector-controlled induction motor drive with online stator and rotor resistance tracking.

In order to investigate the performance of the drive for
parameter variations in rotor resistance

Induction motor parameters.

Stator resistance |

Rotor leakage inductance |

Stator leakage inductance |

Magnetizing inductance |

Rotor resistance |

Moment of inertia ^{2} |

Initially, a 40% error was introduced between

Performance of the drive with and without rotor and stator resistance compensations.

40% step change
in

40% step change
in

Later, simulations were repeated after switching on
only the rotor resistance estimation block with the SRE block switched off, for
the same errors introduced in Figure

Finally, the simulations were carried out with both the
RRE and SRE blocks switched on. The results of torque, rotor flux linkage, and
stator current amplitude are shown for both of the cases, in Figure

However, there was a small but insignificant error of 4.4%, as shown Figure

The Figures

Performance of the induction motor drive with a ramp change in stator and rotor resistance with and without RRE and SRE.

Finally, both
rotor and stator resistance estimators are investigated with both RRE and SRE
switched ON. The estimated rotor resistance has tracked the real rotor
resistance of the motor very well, as the error now drops to 0.3% as in Figure

In
order to verify the proposed stator and rotor resistance estimation algorithms,
a rotor flux oriented induction motor drive was implemented in the laboratory
as shown in Figure

Experimental setup for the resistance identification in induction motor drive.

The experimental setup was built for
the 1.1 kW squirrel cage induction motor around a dSPACE DS1104 controller
board residing in PC, as shown in Figure

Photograph of the experimental setup of the 1.1 kW squirrel cage induction motor drive.

The
induction motor in Table

Induction motor parameters.

Stator resistance |

Stator leakage inductance |

Rotor leakage inductance |

Magnetizing inductance |

Rotor resistance |

Moment of inertia ^{2} |

Rotor resistance measurements.

Measured rotor resistance | Estimated rotor resistance using |
---|---|

the proposed estimator | |

2.62 Ω | 2.51 Ω |

Photograph of the experimental setup of the 3.7 kW slip-ring induction motor drive.

After establishing the validity of the proposed rotor resistance estimation with the slip-ring induction motor, experimental investigations are repeated with the squirrel cage induction motor which was used for the modeling studies.

In order to examine the
capability of tracking the rotor resistance of the induction motor with the
proposed estimator, a temperature rise test was conducted, at a motor speed of
1000 rev/min. The results of

Estimated

Rotor fluxes in

To test
the stator resistance estimation, an additional 3.4 Ωper
phase was added in series with the induction motor stator, with the motor
running at 1000 rev/min with a load torque of 7.4 Nm. The estimated stator
resistance together with the actual stator resistance is shown in Figure

Estimated stator resistance

Stator currents in

The modeling results as described
in Figure

Comparison of stator resistance estimations.

This
paper has presented a new online estimation technique for the rotor resistance

Investigations
carried out in this paper have clearly shown that two ANNs can be used in
estimating

Stator voltage vector in stator reference frame

Magnitude
of the stator current vector

Stator current vector in stator reference frame

Rotor flux linkages estimated by voltage model in stator reference frame

Rotor flux linkages estimated by current model in stator reference frame

Rotor flux linkages estimated by neural network

Stator(rotor) resistance

Neural network weights in rotor resistance estimator

Neural network weights in stator resistance estimator

Cumulative error fuctions

Training coefficients

Momentum constants

Error function vector

Error functions

Rotor speed in rev/minute.