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. 2021 Jun 30;21(13):4512.
doi: 10.3390/s21134512.

A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine

Razvan Mocanu  1 , Alexandru Onea  1 , Constantin Catalin Dosoftei  1
Affiliations

A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine

Razvan Mocanu et al. Sensors (Basel). .

Abstract

The need for protection of electrical machines comes as a demand of safety regulations in the automotive industry as well as a result of the general desire to obtain a robust and reliable electric powertrain. This paper introduces a hybrid method for estimating the temperature of the rotor of an Induction Machine (IM) based on a Nonlinear Autoregressive Network with Exogenous inputs (NARX) used as a prediction function within a particle filter. The temperature of the stator case is measured, and the information is used as an input to a NARX network and as a variable to a thermal process with first-order dynamics which serves as an observation function. Uncertainties of the NARX and thermal model are determined and used to correct the posterior estimate. Experimental data are used from a real IM test-bench and the results prove the applicability and good performance.

Keywords: AC machines; fault diagnosis; neural network applications; recursive estimation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A conceptual illustration of a 48 V electrification system.
Figure 2
Figure 2
The speed-toque characteristic w.r.t to the DC voltage bus.
Figure 3
Figure 3
Illustration of the data acquisition chain.
Figure 4
Figure 4
NARX Neural Network.
Figure 5
Figure 5
The error distribution of the neural network.
Figure 6
Figure 6
The PDF of the observation model.
Figure 7
Figure 7
Block representation of the estimator.
Figure 8
Figure 8
View of the test-bench.
Figure 9
Figure 9
Estimated and actual rotor temperature (test scenario I).
Figure 10
Figure 10
Post estimate temperature deviation (test scenario I).
Figure 11
Figure 11
Shaft angular speed (test scenario I).
Figure 12
Figure 12
Stator current (RMS) (test scenario I).
Figure 13
Figure 13
Rotor current (RMS) (test scenario I).
Figure 14
Figure 14
The estimated and the actual rotor temperature (test case II).
Figure 15
Figure 15
Post estimate temperature deviation (test scenario II).
Figure 16
Figure 16
Shaft angular speed (test scenario II).
Figure 17
Figure 17
Stator current (RMS) (test scenario II).
Figure 18
Figure 18
Rotor current (RMS) (test scenario II).
Figure 19
Figure 19
Estimated and actual rotor temperature (test scenario III).
Figure 20
Figure 20
Post estimate temperature deviation (test scenario III).
Figure 21
Figure 21
Shaft angular speed (test scenario III).
Figure 22
Figure 22
Stator current (RMS) (test scenario III).
Figure 23
Figure 23
Rotor current (RMS) (test scenario III).
Figure 24
Figure 24
The error histogram of the estimated temperatures over the entire test set.
See this image and copyright information in PMC

References

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