Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm
Round 1
Reviewer 1 Report
Dear authors, please, let me know the temperature's influence on your results. I suppose it is significant, but I couldn´t even find the word "temperature" in your excellent text.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors are talking about an interesting topic but there are some major issues
1. I couldn't see the novelty of the proposed work.
2. The literature is rich by the work done in that area and I couldn't see that in the paper.
3. You need to add a table of comparison between the previous work done and what you are proposing to show the importance of your proposed work.
4. There should be a logical explanation for why you got that result based on mathematical background.
5. Hypothesis tests are needed.
6. Your work needs to be validated.
The authors are talking about an interesting topic but there are some major issues
1. I couldn't see the novelty of the proposed work.
2. The literature is rich by the work done in that area and I couldn't see that in the paper.
3. You need to add a table of comparison between the previous work done and what you are proposing to show the importance of your proposed work.
4. There should be a logical explanation for why you got that result based on mathematical background.
5. Hypothesis tests are needed.
6. Your work needs to be validated.
Author Response
Dear Reviewer:
Thank you for your suggestions on the paper. We have carefully read and made modifications to the paper. Below are our responses to all the suggestions.
You provided feedback on the English proficiency of the paper, and suggested that our manuscript should undergo extensive English revisions. Thank you for your reminder. We realized the seriousness of the problem and conducted a thorough examination of the paper, while also making corrections to some English grammar and expressions. Therefore, before replying to your specific comments, we have listed the corrections to the English sentences in the paper.
1, In the abstract, on line 15, “a genetic algorithm optimized by BP neural network (GA-BP).” correct to “the Genetic Algorithm-BackPropagation (GA-BP) neural network.”.
2, On the title of Table1, on line 102, “Table 1. Basic parameter of the external rotor PMA-SynRM.” correct to “Table 1. Basic parameters of the external rotor PMA-SynRM.”.
3, On the title of Table2, on line 179, “Table 2. Intial value rance of each structural variable.” correct to “Table 2. Initial value range of each structural variable.”.
4, In the Figure 6, on line 252, the titles in the X and Y axes have been corrected, “Structure Variablese” correct to “Structure Variables”, and “Integrated Sensitivity Correlation Coefficiente” correct to “Integrated Sensitivity Correlation Coefficients”.
5, On line 400, “4. Anlysis of optimization results” correct to “4. Analysis of optimization results”.
6, On line 401, “Chapter 3” correct to “Section 3”.
7, On line 402, “Chapter 2” correct to “Section 2”.
8, In the references, on line 537, “Manyobjective optimization of ...” correct to “Many objective optimization of ...”.
The above are the corrections we have made to the English sentences in the paper. Below are our responses to specific comments and suggestions.
1, We provide some explanations for the novelty of the proposed work. The paper is technically sound as the introduction of such mathematical applications to the world of motor design is a novelty.
Firstly, the research object of the paper is a permanent magnet assisted synchronous reluctance motor, which has many design structural parameters, and the degree of influence of different parameters on electromagnetic performance varies. In the parameter sensitivity analysis stage, using methods based on Pearson coefficient and comprehensive sensitivity coefficient can save a lot of work time compared to traditional orthogonal design methods, and can effectively improve the accuracy of the analysis results (because orthogonal design requires manual analysis of the data, while the method in the paper uses program control and computer analysis of the data). This point is introduced in the introduction of the paper (In lines 45-53).
Secondly, in the part of fitting the motor model, the paper adopts a genetic algorithm optimized BP neural network and compares it with traditional BP neural networks to show that the method used in the paper has smaller prediction errors on the data. In this regard, the commonly used method is the response surface modeling method. Although this work can also be completed based on optislang, there are shortcomings in both time and accuracy. Therefore, this is a bold innovation of the paper. This point is introduced in the introduction of the paper (In lines 54-62).
Finally, the paper uses NSGA-III to optimize and fit the motor, which is important when dealing with four or more optimization objectives. Although there have been cases of using genetic algorithms for motor optimization design, most of them use traditional genetic algorithms or second-generation multi-objective genetic algorithms. It is undeniable that these algorithms have good results in dealing with single objective optimization or three objective optimization, while they are prone to falling into local optima when dealing with more objective optimizations, which is very detrimental to the design results, and NSGA-III can precisely solve this problem. This point is also stated in the introduction of the paper (In lines 63-70).
2, We provide some explanations for the contribution of the paper to the field of motor design and optimization. The paper contributes to the body of knowledge by proposing the use of genetic algorithms for the optimization of electric motors, something that will become increasingly necessary in the research world when higher power densities or improvements in specific aspects of the design are desired.
It can be said that the composite algorithm proposed in the paper provides an analytical tool for the design and optimization of many complex structures such as permanent magnet assisted synchronous reluctance motors.
Especially when dealing with design problems with a large number of structural parameters, parameter sensitivity analysis can be completed at a faster speed to identify parameters that have a significant impact on the target for optimization. Alternatively, when dealing with design problems with multiple optimization objectives, it is possible to avoid optimization falling into local optima while ensuring simultaneous optimization of multiple objectives.
3, Regarding the suggestion of “You need to add a table of comparison between the previous work done and what you are proposing to show the importance of your proposed work.”, we would like to first thank you for your reminder and then provide some explanations. For this point, we understand that a comparison table needs to be added to illustrate that the composite algorithm proposed in the paper is more suitable for the design of permanent magnet assisted synchronous reluctance motors than other optimization methods.
As we replied to your first two questions, currently most motor designs use orthogonal design method for parameter sensitivity analysis, response surface method for motor modeling and optimization, and a small number of scholars use the second generation multi-objective genetic algorithm in the final optimization stage. The shortcomings of these processing methods in dealing with multi-parameter multi-objective optimization problems have been introduced in the introduction section, The corresponding advantages of this paper are prominent and clear.
However, in order to more intuitively express the advantages of the proposed method in the paper, while maintaining consistency between design parameters and optimization objectives, we adopted the traditional response surface optimization design method to optimize the research object of the paper, and added the comparative content to the paper, located in lines 456 to 474 of the paper.
In addition, we have made modifications to the chapter arrangement of the paper, using the first paragraph of the original conclusion section as a new chapter, “5. Discussion” (On line 431). The other text has been corrected as a new section, “6. Conclusion and Statement”. At the same time, adjustments have been made to the content of Part 6 (In lines 475 to 479), all of which have been marked in the text.
4, Regarding the suggestion of “There should be a logical explanation for why you got that result based on mathematical background.”, we would like to first thank you for your reminder and then provide some explanations. The overall optimization design process of the paper is divided into three parts: 1. Parameter sensitivity analysis; 2. Fitting the motor model; 3. Optimization of motor parameters.
In the first part, we perform parameter sensitivity analysis on the design variables using Pearson coefficients and comprehensive sensitivity coefficients. The obtained data is then set with thresholds and optimized variables are selected. Among them, formulas (9) and (10) in the text are the corresponding mathematical expressions.
In the second part, we used the GA-BP method to fit the sampling data of the motor model, but we forgot the mathematical expression of some important processing. Therefore, thank you for your reminder. We have made corresponding modifications in the paper. We have added the calculation method for the hidden layer of the BP neural network in lines 270 to 278.
In the third part, we use the NSGA-III algorithm to optimize the motor model fitted in the second part, which includes the selection of reference points and the construction of cost functions. The selection of reference points is the foundation of the algorithm, and there is not much introduction in the paper. The construction of cost functions has been introduced in text, located in lines 358 to 361 of the paper. In the process of finding the optimal solution, we use the method of multi-objective weighted trade-off design, and formulas (13) and (14) can precisely illustrate this problem.
5, Regarding the question of hypothesis testing, we appreciate your suggestion and our explanation. The paper mainly studies the proposed composite algorithm and verifies its feasibility through a series of simulation simulations. It is feasible and technically reasonable for optimizing the electromagnetic performance of permanent magnet assisted synchronous reluctance motors. We believe that hypothesis testing generally occurs in research on motor control, but we will also adopt your suggestion and consider this aspect in subsequent research in the paper, hoping to receive your insights again.
6, Regarding the verification of design results, we have conducted extensive numerical simulation experiments and finite element electromagnetic simulation experiments in the paper, as well as comparative experiments to verify that the method proposed in this paper is technically reasonable and advanced. The reason why we did not conduct physical production and verification is that due to limitations in scientific research conditions and funding, it is currently not possible to proceed, but arrangements will be considered in future work. The current method adopted in the paper is validated through a large number of simulation experiments.
Author Response File: Author Response.pdf
Reviewer 3 Report
Please see attached file.
Comments for author File: Comments.pdf
English language is fine.
Author Response
Dear Reviewer:
Thank you for your suggestions on the paper. We have carefully read and made modifications to the paper. Below are our responses to all the suggestions.
1, In the abstract, on line 15, “a genetic algorithm optimized by BP neural network (GA-BP).” has been corrected to “the Genetic Algorithm-BackPropagation (GA-BP) neural network.”.
2, In Introduction, at the end, thank you for your reminder, but we would like to explain this point. The paper has already provided a paragraph with a synopsis on the overall contents of the paper in lines 71-84.
3, On the title of Table1, on line 102, “Table 1. Basic parameter of the external rotor PMA-SynRM.” has been corrected to “Table 1. Basic parameters of the external rotor PMA-SynRM.”.
4, In Figure 1 and 3, thank you for your reminder, but we would like to explain this point. The reason why the text font size in the images is relatively large is to display the meaning of the structure or vector more clearly. We have made adjustments to reduce the font size of the text in the two images.
5, On the title of Table2, on line 179, “Table 2. Intial value rance of each structural variable.” has been corrected to “Table 2. Initial value range of each structural variable.”.
6, In Figure 6, the titles in the X and Y axes have been corrected, “Variablese” has been corrected to “Variables”, and “Coefficiente” has been corrected to “Coefficients”.
7, On line 392, “4. Anlysis of optimization results” has been corrected to “4. Analysis of optimization results”.
8, On line 393, thank you for your reminder, “Chapter 3” has been corrected to “Section 3”.
9, On line 394, thank you for your reminder, “Chapter 2” has been corrected to “Section 2”.
10, The Conclusions sections seem to be long. Thank you for your reminder, but we would like to explain this point. The text in the conclusion section of lines 423-449 is a detailed and comprehensive summary of the entire work, which is different from the overview section in the last paragraph of the introduction. We have split this section, with the text used to summarize the work content of the article as a new section, “5. Discussion”. The other text has been corrected as a new section, “6. Conclusion and Statement”.
11, A statement should be added that mentions explicitly the actual scientific contribution. Thank you for your reminder, but we would like to explain this point. The paper has a similar explanation, but the expression is not clear. It has been corrected and the relevant text is included in the conclusion and statement of Part 6 of the paper.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The paper looks good now but needs more editing for some typos and english language.
The paper looks good now but needs more editing for some typos and english language.
