A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
Round 1
Reviewer 1 Report
The paper is well-written and deals with a fundamental challenge is power systems. The paper outlines the application of appropriate algorithm to effect that proposed algorithm proves to be very effective.
I would suggest that authors work on gathering real data and validate the proposed method for future works. However, I accept the paper with no reservation.
Updated 3.15
Below are my comments in support of the paper being accepted. 1. The paper addresses the condition monitoring and fault detection in power distribution systems. This is a very critical aspect in both the health index of the power grid and the socio economic well being. 2. The paper is very interesting and displays meaningful mathematical and engineering concepts. 3. The novelty of the paper is based on the application of a parametric algorithm to solve the engineering problem. 4. The paper in its current form has explained the methodology very well and must be accepted as such. 5. The conclusions are well-written and are interlinked with the content of the subject. 6. References are well written and entail all relevant information. 7. Figures are visible and the quality is very good. The above comments support the acceptance of the paper. It is my view that the authors have done a very good job in formulating the study.
Author Response
Thank you very much for your comments and recognition of this paper! Your recognition is a great encouragement for our further research. As you have said, integrating with the reality is an important way to validate the effectiveness of the theory. We are now cooperating with power companies to apply the methods proposed in this paper to practical engineering on the basis of collecting authentic data from distribution networks. Thank you very much again for your support!
Reviewer 2 Report
In this manuscript, the authors have proposed a fault location method based on divide-and-conquer (DAC) to solve the inadequacy problem that arises when using the traditional fault section location method based on the optimization model of logic operation. The results indicate that the proposed method has linear time complexity and can accurately locate faults in milliseconds, providing a reference for solving the fault location problem in large distribution networks.
1. It is recommended that the authors provide a figure of the test results for the IEEE33 node distribution network and an explanation of the analysis, and further illustrate the time advantages of the method proposed in this paper.
2. It is suggested that the authors add a comparison table or figure with current research and an explanation of the analysis to further illustrate the superiority of the method proposed in this paper.
3. The description of equation 15 and equation 40 contain unknown symbols and the authors are advised to check and revise the article.
Author Response
Point 1. It is recommended that the authors provide a figure of the test results for the IEEE 33 node distribution network and an explanation of the analysis, and further illustrate the time advantages of the method proposed in this paper.
Response 1. Thank you very much for your suggestion, your comments are important to illustrate the performance advantages of the method proposed in this paper. We have added Section 5.1.3 to illustrate the comparative results of the computational efficiency under IEEE-33 node distribution network and analyze the results. In addition, we analyze the time complexity of various compared algorithms in Section 5.2.2 and summarize the results of the time complexity analysis in Section 5.3 to further demonstrate the advantages of the algorithm's computational efficiency in this paper.
Point 2. It is suggested that the authors add a comparison table or figure with current research and an explanation of the analysis to further illustrate the superiority of the method proposed in this paper.
Response 2. Thank you very much for your suggestion, your comments are important in illustrating the superiority of the method proposed in this paper. We have added Section 5.3, in which we compare the accuracy and computational efficiency of the algorithms in tabular form and explain the results of the comparison to further illustrate the superiority of the performance of the algorithm proposed in this paper.
Point 3. The description of equation 15 and equation 40 contain unknown symbols and the authors are advised to check and revise the article.
Response 3. Thank you very much for your comment, your suggestion is very helpful to improve the readability of the article. As you said, due to an oversight, we did not explain the variable "Pk1" in Equation (A2) and the variables "r1,r2" in Equation (A7), which can cause difficulties for the reader to understand the meaning of the relevant equations. We explain the variable "Pk1" and the variables "r1,r2” in the next paragraph of Equation (A2) and the previous paragraph of Equation (A4), and the equations were reviewed throughout the text to ensure that each variable was explained for as a way to enhance the readability of the paper.
Reviewer 3 Report
After reviewing the manuscript, here are my comments:
1- Please add a flowchart for overal approach of the manuscript to increase its readability.
2- Some parts are too lenghty (e.g., parts 2 and 3). Please remove unnecessary sections or at least relocate these parts to the appendix.
3- In what basis you selected the algorithms to compare the method of this paper? Please reveal the reasons or compare the method with the feature algorithms in the field.
4- Please improve the conclusions and support it by data.
Author Response
Point 1. Please add a flowchart for overal approach of the manuscript to increase its readability.
Response 1. Thank you very much for your suggestion, your comments are very helpful to enhance the readability of the paper. In order to facilitate the analysis of the computational efficiency of the method proposed in Section 4.2, the basic principles of the algorithm are described in the form of pseudocode. However, this approach does not facilitate the reader to understand the execution process of the algorithm. We add a flowchart before the last paragraph of Section 4.1 to help the reader understand how the algorithm is executed.
Point 2. Some parts are too lenghty (e.g., parts 2 and 3). Please remove unnecessary sections or at least relocate these parts to the appendix.
Response 2. Thank you very much for your suggestion, your comments are very helpful to enhance the readability of the paper. We have condensed the content of Sections 2 and 3 of the paper and placed the proof of the theorem in the appendix to make the paper more logical and concise.
Point 3. In what basis you selected the algorithms to compare the method of this paper? Please reveal the reasons or compare the method with the feature algorithms Please reveal the reasons or compare the method with the feature algorithms in the field.
Response 3. Thank you very much for your comment. As described in the analysis of the current state of research in Section 1 of this paper, there are three main types of typical distribution network fault location methods: the first type is based on intelligent optimization algorithm fault location methods, the second type is based on hierarchical optimization models fault location methods, the third type is based on linear integer programming model fault location methods. We have selected one representative algorithm from each of the above types of fault location methods as a comparison algorithm for simulation testing to verify the performance advantages of the proposed method in this paper. The issues you pointed out are critical, and we add the reasons for selecting these comparison algorithms in the first paragraph of Section 5.
Point 4. Please improve the conclusions and support it by data.
Response 4. Thank you very much for your suggestion, which is important to improve the credibility of the conclusions. We have enriched the conclusions by adding the reasons why the accuracy of the algorithms is not affected by network size in Section 6, paragraph 3, and by adding comparative data from simulation tests to confirm the conclusions in Section 6, paragraphs 2 and 3.
Reviewer 4 Report
1. It is recommended to include the proof of the theorem in the appendix.
2. "The pseudocode for the algorithm is shown as Figure 8.", it is suggested to replace pseudocode with program flow chart.
3. It is recommended to demonstrate the superiority of this method by comparing it with the latest algorithms.
Author Response
Point 1. It is recommended to include the proof of the theorem in the appendix.
Response 1. Thank you very much for your suggestion, your comments are very helpful in enhancing the readability of the article. We have placed the proofs of the theorems in the appendix to improve the fluency of the paper.
Point 2. "The pseudocode for the algorithm is shown as Figure 8.", it is suggested to replace pseudocode with program flow chart.
Response 2. Thank you very much for your suggestion, your comments are very helpful to enhance the readability of the article. In order to facilitate the analysis of the computational efficiency of the proposed method in Section 4.2, the basic principles of the algorithm are described in the form of pseudocode. As you said, this approach does not facilitate the reader to understand the execution process of the algorithm. We have added a flowchart before the last paragraph of Section 4.1 to help the reader understand how the algorithm is executed.
Point 3. It is recommended to demonstrate the superiority of this method by comparing it with the latest algorithms.
Response 3. Thank you very much for your suggestion. The algorithms selected for comparison in this paper are from recent publications, and we have simulated and tested the algorithms in IEEE 33-node distribution networks and IEEE 69-node distribution networks, and compared their accuracy, fault tolerance, and computational efficiency. You have asked questions that are critical to illustrate the performance advantages of the algorithms in this paper, and we add the provenance of the participating comparison algorithms in the first paragraph of Section 5, and the specific fault location strategies of these algorithms in Table 5.
Round 2
Reviewer 3 Report
Thank you for making the changes in the manuscript.
In the third comment of the last review, I meant why the selected methods for comparison can be a good representative for its category. By the way, thank you for your efforts to revise the manuscript.
Author Response
Dear Reviewer,
We feel great thanks for your comments and valueble suggestions. We have carefully considered the comments and revised our manuscript. Please see the attachment for the detailed point-by-point response to your comments.
Best wishes.
Author Response File: 
Author Response.docx
