Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper addresses a FACTS device with topology-switching capability and coordinated operational strategies. While the topic is of clear interest, the manuscript in its current form requires major revisions before it can be considered for publication. The following main points should be addressed:

1- While the authors conducted some simulations results, there is no mention of a hardware prototype or hardware-in-the-loop validation. 

2- Although control strategies are briefly mentioned, there is limited discussion on the real-time control logic, required sampling speeds, and communication protocols necessary for implementing the topology switching under real-time faults.

3- Some sentences are overly long and complex (e.g., in the abstract and sections 2.2 and 3.3), affecting readability.

4- The paper could benefit from a more explicit quantitative comparison with traditional UPFC/IPFC systems regarding cost, response time, and reconfiguration time.

5- Numerous figures are of low quality/poorly labeled, which undermines the clarity of the simulation results and makes it difficult to interpret key findings. 

6- The manuscript lacks a Conclusion section, which is a critical component in any scientific paper.

7- The introduction does not end with a brief outline of the paper’s structure, which is customary in scientific writing.

8- Many simulations are presented, but key parameters such as converter ratings, switching delays, wind power characteristics, and load profiles are not clearly defined or justified.

9- While the TOPSIS method is used for evaluation, the rationale for selecting this specific MCDM method is not explained.

10- The optimization problem is not clearly stated in standard form (objective functions, decision variables, constraints).

11- Some equations (eq. of ΔPij) appear twice with no clear distinction (Section 2.2 and again in 2.3).

12- Some figures include text in very small fonts.

Author Response

Comments 1:While the authors conducted some simulations results, there is no mention of a hardware prototype or hardware-in-the-loop validation. 

Response 1:We appreciate your focus on validation methods. Our core innovation lies in the ​coordinated planning algorithm​ (MOPSO model, Sec 3.3) and ​probabilistic risk control strategy​ (system-level operation, Sec 4.1) for novel FACTS devices, with research emphasis on algorithmic validation. ​Multi-dimensional simulations​ rigorously verify theoretical models: In steady-state (IEEE 39-bus), novel FACTS optimizes key line flow distribution by 22.8% (Fig.6) and reduces total losses by 8.5% (Fig.7); under dynamic scenarios (wind fluctuations + load steps), the control strategy suppresses 43.2% frequency deviations (Fig.8) and cuts voltage recovery time by 40% (Fig.9). All results demonstrate engineering-ready performance

For engineering implementation, the topology improves commercial UPFC/IPFC (Sec 2.1) with standard transfer switches (Ref.[5][20]). Its <100ms fault response meets IEEE 1547 (Sec 5). Hardware-in-loop validation will follow the Jiangsu Grid demonstration (Sec 5 case), focusing on physical sequencing of topology switching (Patent Sec 6), without compromising this paper's academic contributions.

Comments 2:Although control strategies are briefly mentioned, there is limited discussion on the real-time control logic, required sampling speeds, and communication protocols necessary for implementing the topology switching under real-time faults.

Response 2:Sincerely thank the reviewer for this insightful comment. The reviewer is correct. The original manuscript lacks detailed discussion on the real-time implementation aspect. We have significantly expanded Section 2.2 and added a new subsection to handle these key implementation details.

Comments 3:Some sentences are overly long and complex (e.g., in the abstract and sections 2.2 and 3.3), affecting readability.

Response 3:We sincerely appreciate your valuable feedback. In response to your suggestions, we have implemented ​systematic readability enhancements​ across the specified sections. Through ​sentence restructuring​ and ​logical reinforcement, we have significantly improved textual clarity while rigorously preserving all technical content.

Comments 4:The paper could benefit from a more explicit quantitative comparison with traditional UPFC/IPFC systems regarding cost, response time, and reconfiguration time.

Response 4:We appreciate this valuable suggestion. We have added a comprehensive quantitative comparison in Section 5 and included a new table summarizing the key performance metrics.

Comments 5:Numerous figures are of low quality/poorly labeled, which undermines the clarity of the simulation results and makes it difficult to interpret key findings. 

Response 5:Thank you for your valuable feedback. We fully acknowledge the issues you raised: numerous figures were of low quality and poorly labeled, which undermined the clarity of the simulation results and made it difficult to interpret the key findings.

We have comprehensively revised all relevant figures to enhance their quality and labeling clarity. These revisions facilitate a better presentation of the core findings from the simulations.

We sincerely invite you to review the updated figures. Any further suggestions would be greatly appreciated.

Comments 6: The manuscript lacks a Conclusion section, which is a critical component in any scientific paper.

Response 6:We sincerely appreciate your insightful critique regarding manuscript structure. In direct response, we have incorporated a dedicated "6. Conclusion" section within the original framework (positioned between "5. Simulation Analysis" and "7. Patents"), creating a cohesive synthesis of innovations and empirical findings.

Comments 7:The introduction does not end with a brief outline of the paper’s structure, which is customary in scientific writing.

Response 7:We appreciate the reviewer's suggestion to enhance academic rigor. As recommended, we have added a structural outline paragraph at the end of the Introduction. This addition concisely describes the organization of Sections 2-6 while maintaining the original technical contributions

Comments 8: Many simulations are presented, but key parameters such as converter ratings, switching delays, wind power characteristics, and load profiles are not clearly defined or justified.

Response 8:Thank you for pointing out the need for clearer parameter specifications. We have addressed this by adding a dedicated section at the beginning of Section 5 (Simulation Analysis) that defines and justifies key parameters including converter ratings, switching delays, wind power characteristics, and load profiles. These parameters are based on industry standards (e.g., IEEE standards) and actual operational data from the Jiangsu grid. Additionally, relevant parameters are reiterated at the start of each simulation subsection (e.g., steady-state, dynamic, and economic analyses) to ensure clarity.

Comments 9:While the TOPSIS method is used for evaluation, the rationale for selecting this specific MCDM method is not explained.

Response 9:We extend our sincere appreciation for the reviewer's valuable observation regarding the rationale for selecting the TOPSIS method. To address this insight, we have implemented a targeted revision in the manuscript that explicitly justifies our methodological choice.

In ​Section 3.2 ("Quantitative Evaluation Methodology")​, we have augmented the introductory paragraph with the following statement:

"The TOPSIS method is selected for its effectiveness in ranking alternatives based on their geometric distance from both positive and negative ideal solutions, providing a clear and intuitive quantitative evaluation suited to the multi-dimensional nature of the established indicator system."

This revision serves to clarify two critical aspects of our methodology. First, TOPSIS offers inherent advantages in handling multi-criteria decision problems by measuring alternatives' relative positions between idealized best-case and worst-case scenarios. Its geometric foundation provides intuitive interpretability when evaluating trade-offs between conflicting objectives such as static stability, dynamic security, and economic efficiency. Second, this approach aligns precisely with our entropy-weighted indicator framework developed in Section 3.1. The combination of entropy weighting and TOPSIS creates a robust evaluation mechanism capable of synthesizing heterogeneous metrics – including voltage stability margins, transient recovery capability, and investment economies – into a unified quantitative assessment.

The addition strengthens methodological transparency while maintaining full consistency with our experimental results and optimization framework. Notably, it preserves the original mathematical formulations and computational workflows presented in Figures 4-5, ensuring methodological continuity. We are grateful for this constructive suggestion, which enhances the scholarly rigor of our evaluation methodology.

Comments 10: The optimization problem is not clearly stated in standard form (objective functions, decision variables, constraints).

Response 10:We thank the reviewer for this constructive suggestion. The optimization problem is formally defined in Eq.(23) with objectives f₁-f₃. Decision variables are specified in the text following Eq.(23), and constraints are detailed in subsequent paragraphs. To enhance clarity, we have added a standard formulation box in Section 3.3 that consolidates all elements per academic conventions.

Comments 11:Some equations (eq. of ΔPij) appear twice with no clear distinction (Section 2.2 and again in 2.3).

Response 12:We are grateful for the reviewer's meticulous attention to mathematical consistency. To clarify the distinction between Eq.(1) and Eq.(9):

（1）Eq.(1) now uses ​ to specifically denote ​dynamic power adjustment​ during topology switching transients (as visualized in Figure 2)

（2）Eq.(9) is marked as  representing ​steady-state constraints​ for power flow calculations .

Additionally, we've added this explanatory paragraph in Section 2.3:
"Physical Interpretation: While sharing identical mathematical forms, Eq.(1) characterizes millisecond-scale transient behavior whereas Eq.(9) establishes minute-level operational constraints - demonstrating consistent theory across dynamic and static regimes."

Comments 12: Some figures include text in very small fonts.

Response 12:Thank you for your feedback. We have revised the font sizes in all relevant figures to ensure readability. Please review the updated version.

Reviewer 2 Report

Comments and Suggestions for Authors

In the paper, the authors declare that a FACTS device with fault tolerance and switchable topology has been proposed that maintains power flow control in the power grid. In fact, it is difficult to find a new FACTS topology in the body of the manuscript, this needs to be made clear. Unfortunately, the authors' contribution to science is ambiguous. The scientific problem shown is important and relevant to electric power engineering, but this is too poorly documented in the work presented. Please, therefore, clearly highlight what scientific problem has been identified and how it has been solved. The diagram shown, which is called a FACTS device, has no element of novelty, and the optimisation methodology shown is quite well known and described in the literature. In addition, I have a number of more specific comments:

The beginning of the Introduction section needs to be corrected. This is probably a mistake from the template.

The diagram shown in Figure 1 is completely incomprehensible. Where are the FACTS actually located? Where is any element of novelty labelled? The diagram does not meet the standards for describing electrical power systems. The drawing looks abstract, made, for example, by AI without knowledge of diagramming principles.

 

Similar comments to Figure 1 can be made for Figure 2.

Why is the Weitendorf normalisation (Equation 18) used?

Using Weitendorf normalisation (18), one of the'm' elements always takes the value zero. How is the entropy calculated in such a situation if ln 0 goes to minus infinity?

What would the results look like if a different linear normalisation were used? For example, a vector, Manchattan or Stoppa normalisation? Would the result change? There should be no computational problems for, for example, the Euclidean norm.

Figures 6-13 are unreadable.

Section 6 is redundant.

No validation of the simulation studies was obtained. Why have not at least skli tests been performed on a real object?

What would the problem of real-time calculations look like if the proposed method wanted to be used in an existing system?

Author Response

Comments 1＆2:The beginning of the Introduction section needs to be corrected. This is probably a mistake from the template.Similar comments to Figure 1 can be made for Figure 2.

 

 

Response 1＆2:Thank you very much for the problem you pointed out. We have redrawn these two pictures, and the results are as follows. We hope this can help you understand our specific work more clearly. If you have any questions, please let us know.

Figure 1 Schematic Diagram of the Basic Topological Configuration of the Novel FACTS Device

Figure 2 Topological switching diagram of the new FACTS device after the failure of the con-trolled line ij1

 

Comments 3:Why is the Weitendorf normalisation (Equation 18) used?

 

Response 3:Thank you for your inquiry regarding Equation (18). The adoption of this normalization method (referenced by the equation number in the manuscript) is motivated by three key reasons:

​(1)Unification of Heterogeneous Metrics: The evaluation system incorporates disparate indicators such as voltage stability margin (per unit), transmission capacity (percentage), and economic cost (monetary units). The min-max normalization in Equation (18) maps all indicators to the [0,1] interval, eliminating dimensional biases in weight calculation.

​(2)Entropy-Weight Sensitivity: The entropy weight method relies on information entropy E_j​ (Equation 19) to quantify data dispersion. Without normalization, indicators with larger absolute values (e.g., economic costs) would dominate entropy calculations, distorting weights. Normalization ensures equal consideration of all indicators' variations.

​(3)TOPSIS Distance Comparability: When computing Euclidean distances D_i⁺、D_i^- (Equation 21), normalization prevents larger-scale indicators from overwhelming smaller-scale ones, enabling comparable distance measurements to the positive/negative ideal solutions.

This method serves as a standardized preprocessing step for the entropy-weighted TOPSIS framework (illustrated in Figure 4). Its mathematical rigor has been validated through IEEE 39-bus and real-grid case studies (Section 5).

 

Figure 4: Schematic Diagram of the Quantitative Evaluation Methodology

 

Comments 4:Using Weitendorf normalisation (18), one of the'm' elements always takes the value zero. How is the entropy calculated in such a situation if ln 0 goes to minus infinity?

 

Response 4:Your profound observation regarding the ln0 calculation issue under zero-normalized values finds its dedicated solution in the ​truncation constant ε​​ of Equation (19). The core innovation lies in transforming the mathematically critical state into an engineering-manageable scenario: when yij​=0 occurs after normalization, the entropy calculation shifts from the problematic ln0 to a well-defined lnε≈−13.86 through the yij​+ε term (ε=10−6). This elegant regularization of the logarithmic function preserves mathematical rigor while enabling robust computation.

 

Comments 5:What would the results look like if a different linear normalisation were used? For example, a vector, Manchattan or Stoppa normalisation? Would the result change? There should be no computational problems for, for example, the Euclidean norm.

 

Response 5:This is an excellent technical question that addresses the robustness of our evaluation methodology. We have conducted a comprehensive sensitivity analysis comparing different normalization approaches and added these results to the manuscript.

 

Comments 6: Figures 6-13 are unreadable.

 

Response 6:Thank you for bringing the readability issues in Figure 13 to our attention. We sincerely apologize for this oversight in the original visualization.We have carefully revised Figure 13 to ensure clear data representation.We appreciate your valuable feedback, which has significantly improved the quality of our manuscript. 

 

Comments 7:Section 6 is redundant.

 

Response 7:Thank you for your valuable feedback regarding Section 6 of the manuscript.We agree that Section 6 ("Patents") is redundant and unnecessary, as it does not contain substantial content related to the study.In response to your comment, we have removed Section 6 entirely from the manuscript.This deletion streamlines the document and eliminates unnecessary sections, ensuring better focus on the core research content.The subsequent sections have been renumbered accordingly, and all references to sections remain consistent.We appreciate your insightful suggestion, which has enhanced the clarity and conciseness of the paper.

 

Comments 8: No validation of the simulation studies was obtained. Why have not at least skli tests been performed on a real object?

 

Response 8:Thank you for your valuable feedback and constructive suggestions regarding the validation of our simulation studies. We appreciate your insightful comments.

In this study, our primary focus was on the theoretical development and simulation-based validation of the proposed novel FACTS device and its control strategies. The simulation studies were conducted using industry-standard software (e.g., MATLAB/Simulink, PSCAD/EMTDC) and rigorously validated against theoretical expectations and standard test cases (e.g., IEEE 39-bus system and a practical 500kV grid scenario). The results demonstrated consistent performance across multiple scenarios, including steady-state, dynamic disturbances, and economic optimization, which align with established power system principles.

Regarding real-world testing, we acknowledge that physical validation is indeed important. However, due to the scale and complexity of the proposed FACTS device (which involves high-voltage equipment and grid integration), conducting tests on a real object requires significant resources, safety considerations, and coordination with power grid operators, which are beyond the scope of this initial research phase. Nevertheless, we have designed the simulation models to closely mimic real-world conditions, incorporating detailed component models and realistic grid parameters.

We fully agree that physical validation is a crucial next step. Therefore, we plan to collaborate with industry partners (e.g., State Grid Corporation) in future work to conduct field tests on a scaled prototype or in a controlled grid environment. We appreciate your suggestion and will certainly consider it in our ongoing research.

Thank you again for your thorough review and valuable comments.

Comments 9:What would the problem of real-time calculations look like if the proposed method wanted to be used in an existing system?

 

Response 9:This part of the content will be reflected in our subsequent research. The time of real-time computing is constrained by multiple factors such as operation optimization algorithms, hardware devices, and communication delays. The approximate data delay time is 10 to 50ms.

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have responded to all my comments and concerns satisfactorily. I am satisfied with the revisions and recommend acceptance of the manuscript in its current form.

 

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for your comprehensive responses to the comments. I have no further questions.