A Parameter-Free Method for Estimating the Stator Resistance of a Wound Rotor Synchronous Machine

Round 1

Reviewer 1 Report

The paper is well written and well documented.

Good literature review, with many references. Good quality pictures, although pictures 15 to 19 have less quality - probably due JPEG format.

The reviewer assumed all equations were thoroughly checked by the authors.

There is a possible error in the file: sometimes, figure 14 is not visible.

One question remains: Is the method applicable to permanent-magnet machines?

Author Response

Dear Prof. Hana Yang

 

Thank you for allowing us to submit a revised draft of our manuscript titled “A Parameter-free Method for Estimating the Stator Resistance of a Wound Rotor Synchronous Machine”. We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on our manuscript. We are grateful to the reviewers for their insightful comments on our paper. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript.

Here is a point-by-point response to the reviewers’ comments and concerns.

Yours sincerely,

Authors of the paper

 

The following color codes have been chosen to improve the clarity of the correction:

• Text in blue: comment of the reviewer and the associate editor
• Text in black: author reply to comments and question
• Text in red: changes operated in the new version of the paper

 

 

COMMENTS OF REVIEWER #1

Comments and Suggestions for Authors

The paper is well written and well documented. Good literature review, with many references. Good quality pictures, although pictures 15 to 19 have less quality - probably due JPEG format.

The reviewer assumed all equations were thoroughly checked by the authors.

There is a possible error in the file: sometimes, figure 14 is not visible.

First, we would like to thank the respected reviewer for his valuable suggestions and comments.

In the revised manuscript, we have replaced the figures with higher quality ones and Figure 14 in a different format to correct the illustration problem.

Figure 14 : Simulation results: Estimated resistance by Kalman observer when errors in the parameters are added

One question remains: Is the method applicable to permanent-magnet machines?

Yes, in fact, since wound rotor synchronous machine (WRSM) and permanent magnet synchronous machines (PMSM) have a similar structure, this method can be implemented for the PMSM as well with no modification. For other types of machines, further studies are needed, which can be the subject of future investigations.

This paper is written for a wound rotor synchronous machine because the machine studied is a wound rotor machine designed by Valeo for the automotive industry, and we could test the algorithm on it.

Author Response File: Author Response.pdf

Reviewer 2 Report

Overall, the paper  is nicely written. some improvements can be made. there are a few issues that the can authors address : 

Line 171: Eq. 13 has only three rows. but the line mentions use of a "fourth row".

Figure 14 cannot be seen in the PDF

You have not given any examples of works where direct measurement methods are used, and did not cite your claims regarding the disadvantages of direct measurement methods

You have given a disproportionate background on model based estimation methods (18 references), when your paper is based on signal injection method, for which you gave only 8 references.

Similar to the above comment, you have made comparisons of your methodology (signal injection method) to model based estimation method using Kalman filters.  However , you have not offered comparisons of your method to other signal injection methods.

You used the dSPACE Microlabbox; this can easily make the KF estimation in real-time and high speed, despite the computational requirements of KF. Therefore, the disadvantages you stated for KF is not relevant.   

Finally, you have not indicated if methodology similar to yours (which looks promising), can be used to estimate other dynamic electric motor parameters. 

The conclusion can be improved. Concise quantitative presentation of the results instead of a purely descriptive presentation is recommended.

 

Author Response

Dear Prof. Hana Yang

 

Thank you for allowing us to submit a revised draft of our manuscript titled “A Parameter-free Method for Estimating the Stator Resistance of a Wound Rotor Synchronous Machine”. We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on our manuscript. We are grateful to the reviewers for their insightful comments on our paper. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript.

Here is a point-by-point response to the reviewers’ comments and concerns.

Yours sincerely,

Authors of the paper

 

The following color codes have been chosen to improve the clarity of the correction:

• Text in blue: comment of the reviewer and the associate editor
• Text in black: author reply to comments and question
• Text in red: changes operated in the new version of the paper

 

 

COMMENTS OF REVIEWER #2

Comments and Suggestions for Authors

Overall, the paper is nicely written. some improvements can be made. there are a few issues that the can authors address :

Line 171: Eq. 13 has only three rows. but the line mentions use of a "fourth row".

First, we would like to thank the respected reviewer for his valuable and helpful suggestions and comments.

Thank you for this remark, it has been corrected by the “third raw”.

Figure 14 cannot be seen in the PDF

In the revised manuscript, we have replaced Figure 14 with a different format to correct the illustration problem. Also, some figures have been replaced whit higher-quality ones.

You have not given any examples of works where direct measurement methods are used and did not cite your claims regarding the disadvantages of direct measurement methods.

Direct measurement methods are usually used as a pre-identification or as an approximation of the machine parameters. These measurements are made under certain conditions, such as a known temperature, etc. Since the resistance does not remain constant after the machine is running or after connecting the driver (which may add a series resistance to the stator resistance from the controller's point of view), this is not an accurate method. 

The other disadvantage is that the machine usually must be disconnected from the drivers (offline measurement) and settings and then the measurements are made.

Since these methods do not present a great novelty, there are not many papers to mention.

You have given a disproportionate background on model based estimation methods (18 references), when your paper is based on signal injection method, for which you gave only 8 references.

Similar to the above comment, you have made comparisons of your methodology (signal injection method) to model based estimation method using Kalman filters.  However , you have not offered comparisons of your method to other signal injection methods.

Basically, there are not many references for estimating the stator resistance for a wound rotor synchronous machine. We have mentioned some signal injection methods to estimate other parameters of the machine. Such as High-frequency signal injection methods are mostly used for the initial detection of rotor position [24], [25] or estimation of the magnetization state [22], rather than for estimation of stator resistance. Because they are sensitive to variations in machine parameters, since at high frequencies, the reactance is more important than the resistance. In addition, the skin effect at high frequencies can also be significant and must be considered in the analysis [20].

Some other references that use signal injection methods for estimating stator resistance are described in the introduction section. For example, a low-frequency injection-based method for estimating the stator resistance for PMSMs is proposed in [21], which is a completely model-based method.

Among the few signal injection methods, there were limited ones that estimated the stator resistance, but they were mainly for induction motors and not for synchronous wound rotors. Therefore, the authors decided to use one of the well-known, standard methods such as Kalman observer to make a comparison, which would be more concrete for the readers.

You used the dSPACE Microlabbox; this can easily make the KF estimation in real-time and high speed, despite the computational requirements of KF. Therefore, the disadvantages you stated for KF is not relevant.  

The authors agree with the reviewer's point that the dSPACE MicroLabBox can perform KF estimation in real-time and with high speed. However, our study targets low-end microcontrollers commonly used in automotive applications. While the MicroLabBox was used during development, we aimed to minimize the real-time computational requirements for the final target.

Finally, you have not indicated if methodology similar to yours (which looks promising), can be used to estimate other dynamic electric motor parameters.

The idea of this method was born when we noticed that our model for WRSM predicts that an error on the stator resistance affects the q-component of the estimated flux linkage while signal injection whether the other parameters are accurate or not. This has been confirmed during experimental tests in which we observed a relationship between over-estimation/under-estimation of the stator resistance in the observer equations and the variations of the estimated flux in the q-axis when iq is constant. This led us to develop an algorithm to use this phenomenon to estimate the stator resistance. This algorithm is correct only for the stator resistance, but in future work it would be interesting to study the possibility of using it for other parameters.

The conclusion can be improved. Concise quantitative presentation of the results instead of a purely descriptive presentation is recommended.

Thanks for your suggestion, the following part is added in the conclusion of the revised manuscript:

A comparison with the Kalman observer also illustrated the advantage of the proposed estimator. The results demonstrated the sensitivity of the model-based estimators and it was observed that in this type of estimators, an error in one of the model parameters can lead to a significant error in the estimation of other parameters/variables, up to few tens of percent error. While the proposed estimator in this paper is completely independent of the model parameters and so immune to these errors. However, the Kalman observer can estimate the stator resistance in a short time (less than 0.5 ms), while the proposed method cannot be as fast due to the nature of the low-frequency injection method which can take few seconds.

Author Response File: Author Response.pdf

Reviewer 3 Report

I congratulate the authors on a job well done and recommend the paper for acceptance after minor revision. Some comments and suggestions are given here:

 

- In the introduction, the objectives of the article should be clarified

- Specify the reference of the dSPACE board to use during the experimental validation.

- I propose adding a list of the abbreviations.

- More references to parameter estimation papers should be included, like:

A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor. International Journal of Dynamics and Control, 10(6), 2095-2110.

A New Robust Direct Torque Control Based on a Genetic Algorithm for a Doubly-Fed Induction Motor: Experimental Validation. Energies, 2022, vol. 15, no 15, p. 5384.

Predictive Torque and Direct Torque Controls for Doubly Fed Induction Motor: A Comparative Study. In : Digital Technologies and Applications: Proceedings of ICDTA’22, Fez, Morocco, Volume 1. Cham : Springer International Publishing, 2022. p. 825-835.

- Please compare the proposed solution with recent results.

 

Author Response

Dear Prof. Hana Yang

 

Thank you for allowing us to submit a revised draft of our manuscript titled “A Parameter-free Method for Estimating the Stator Resistance of a Wound Rotor Synchronous Machine”. We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on our manuscript. We are grateful to the reviewers for their insightful comments on our paper. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript.

Here is a point-by-point response to the reviewers’ comments and concerns.

Yours sincerely,

Authors of the paper

 

The following color codes have been chosen to improve the clarity of the correction:

• Text in blue: comment of the reviewer and the associate editor
• Text in black: author reply to comments and question
• Text in red: changes operated in the new version of the paper

 

 

COMMENTS OF REVIEWER #3

Comments and Suggestions for Authors

I congratulate the authors on a job well done and recommend the paper for acceptance after minor revision. Some comments and suggestions are given here:

- In the introduction, the objectives of the article should be clarified

First of all, we would like to thank the respected reviewer for his valuable and helpful suggestions.

This paper presents a new online method based on low-frequency signal injection to estimate the stator resistance of a Wound Rotor Synchronous Machine (WRSM). The major benefit of this method is the estimation of the stator resistance without the need for information about the values of the motor parameters (including d- and q-axis inductances, etc.) while the motor is running.

The following part has been added in the conclusion of the revised manuscript:

This paper presents a method for estimating the stator resistance without the need for information about the values of the motor parameters, such as d- and q-axis inductances, and rotor-stator mutual inductance. The proposed estimator is presented after a review of some existing estimation methods.

- Specify the reference of the dSPACE board to use during the experimental validation.

The used dSPACE system for this paper is “dSPACE MicroLabBox” as is added in the revised paper.

 

- I propose adding a list of the abbreviations.

A nomenclature is added to the revised paper.

 

- More references to parameter estimation papers should be included, like:

A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor. International Journal of Dynamics and Control, 10(6), 2095-2110.

A New Robust Direct Torque Control Based on a Genetic Algorithm for a Doubly-Fed Induction Motor: Experimental Validation. Energies, 2022, vol. 15, no 15, p. 5384.

Predictive Torque and Direct Torque Controls for Doubly Fed Induction Motor: A Comparative Study. In : Digital Technologies and Applications: Proceedings of ICDTA’22, Fez, Morocco, Volume 1. Cham : Springer International Publishing, 2022. p. 825-835.

Thank you for your suggestions, we have added 4 more references.

1. Tang, J.; Yang, Y.; Blaabjerg, F.; Chen, J.; Diao, L.; Liu, Z. Parameter identification of inverter-fed induction motors: A review. Energies 2018, 11, 2194.
2. Mahfoud, S.; Derouich, A.; El Ouanjli, N.; Mossa, M.A.; Bhaskar, M.S.; Lan, N.K.; Quynh, N.V. A New Robust Direct Torque Control Based on a Genetic Algorithm for a Doubly-Fed Induction Motor: Experimental Validation. Energies 2022, 15, 5384
3. Haghgooei, P.; Corne, A.; Jamshidpour, E.; Takorabet, N.; Khaburi, D.A.; Nahid-Mobarakeh, B. Current sensorless control for a wound rotor synchronous machine based on flux linkage model. IEEE Journal of Emerging and Selected Topics in Power Electronics 2021, 10, 4576–4586.
4. Lazcano, U., Poza, J., Garramiola, F., Rivera, C. A., & Iturbe, I. (2022). Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines. Applied Sciences, 12(17), 8812.
5. Baghli, L., Al-Rouh, I., & Rezzoug, A. (2006). Signal analysis and identification for induction motor sensorless control. Control engineering practice, 14(11), 1313-1324.

- Please compare the proposed solution with recent results.

As most of the presented methods for resistance estimation are based on the machine model, and signal injection based methods are mostly used to estimate other parameters, the authors decided to use one of the well-known standard methods such as Kalman observer to make a comparison, which would be more concrete for the readers. However, the following part has been added in the conclusion of the revised manuscript:

A comparison with the Kalman observer also illustrated the advantage of the proposed estimator. The results demonstrated the sensitivity of the model-based estimators, and it was observed that in this type of estimators, an error in one of the model parameters can lead to a significant error in the estimated parameter (up to a few tens of percent error). While the proposed estimator is completely independent of the model parameters. However, the Kalman observer can estimate the stator resistance in a short time (less than 0.5 ms), while the proposed method cannot be as fast due to the nature of the low-frequency injection method which can take a few seconds.

Author Response File: Author Response.pdf