Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth

Round 1

Reviewer 1 Report

1. When pole-to-ground fails, the fault current will flow from the fault point to the earth. Then, where will it flow? Does capacitor C affect the magnitude of the fault current in Figure 2b?
2. Does high resistance ground and low resistance ground cause different fault currents? What is the value of Rf in Figure 2b? Can the method proposed be applied to high resistance ground faults?
3. Can the method proposed be applied to three-phase ground faults?

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The authors have proposed a novel method for the recognition of Voltage Source Converter Direct Current (VSC DC) Cable faults based on Bayesian Functional Data Analysis (FDA). In particular, the work focuses on the recognition of pole-to-pole and pole-to-ground faults considering model uncertainty.

In the introduction a comparison of the typical approaches used for fault detection in power and energy systems is reported. Afterwards, a deep literature analysis of the cable fault modelling techniques is presented. Then, the proposed algorithm used for the fault detection and its mathematical background is clearly explained. Eventually, the proposed algorithm is applied to a simulation model of a VSC DC cable. The results show that voltage measurements are better diagnostic indicators compared to current measurements for the detection of pole-to-pole and pole-to-ground faults.

In order to consider the paper acceptable for publication, the following points must be corrected:

• Please write all the abbreviations within the text in the form: Voltage Source Converter (VSC). This could help to enhance the readability of the work.
• Please describe all the symbols reported in Figure 2 within the text or within the caption of the figure.
• Figure 4b: please write Current [kA].
• Table 1 and 2: please describe all the quantities of the first line within the text and express the unit of measurement.
• Figure 6 and 9: please report the unit of measurement.
• Line 323: please write figure 7.
• Figure 7 and 10: please write Time [ms].
• Figure 8b: please correct the y-label (Voltage [kV]).
• Line 351: please write Figure 10.

In addition, I have detected some language errors that should be corrected:

• Line 20: correct wich.
• Line 33: remove the “.” After “variants)”.
• Line 126, 134, 206: correct the sentences.
• Caption Table 4: correct ooverlaping.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

This paper presentes a contribution of using a methodology for purpose of signal type recognition. Some important issues should be explained before possible acceptance:  

1. There are a lot of irregularities in the article, such as inconsistent capitalization of keywords. Author needs to find and correct these errors
2. What is the significance of u_n representation of Eq. 3 and what is the calculation method?
3. The y_i mentioned at the end of Eq. 3 is not used in the formula in this paper. Why does the author propose this parameter?
4. As an important parameter, u_n should be explained and the calculation method should be explained. However, they are not explained in Eq.3 nor in case study.
5. There is no legend in a large number of pictures, which leads to the inability to understand the meaning of the picture.
6. What is the known information in the table given by reference? And what’s the means of hdi_3%, hdi_97%, mcse_mean, mcse_sd, ess_bulk and ess_tail r_hat?
7. Recent Bayesian and fault diagnosis related work could be referred to, Data-driven early fault diagnostic methodology of permanent magnet synchronous motor, Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study, Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs.
8. There seems to be no Fig. 10(c) in the paper.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Good with the revision.