Title: Dataset for Reliability Modelling and Evaluation of MMCs (modular multilevel converters) under Different Redundancy Schemes

Citation
Guo J, Wang X, Liang J, et al. (2017). Dataset for Reliability Modelling and Evaluation of MMCs (modular multilevel converters) under Different Redundancy Schemes. Cardiff University. http://doi.org/10.17035/d.2017.0038063164


This data is not currently available because: Intent to publish project results
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Cardiff University Dataset Creators

Dataset Details
Publisher: Cardiff University
Date (year) of data becoming publicly available: 2017
Data format: .xlsx
Estimated total storage size of dataset: Less than 100 megabytes
Number of Files In Dataset: 5
DOI: 10.17035/d.2017.0038063164

Description

The dataset includes the basic parameters and results for the reliability modelling and evaluation of modular multilevel converters (MMCs) under different redundancy schemes.

Data shown in the file ”Basic parameter.xlsx” are the parameters of converters. And the study was based on those parameters.

Data shown in the file ”Results_Case A.xlsx” are the results of case A, including the reliability of arms in the load-sharing mode obtained by using different models (as shown in sheet 1), and the reliability of arms under passive schemes calculated by using different models (as shown in sheet 2).

Data shown in the file ”Results_Case B.xlsx” are the results of case B, including the MTTF of arms in sheet 1, and the reliability of MMCs in sheet 2.

Data shown in the file ”Results_Case C.xlsx” are the results of case C, i.e. the MTTF of converter arms under different redundancy schemes with failure rate ratio varying from 0.1 to 10.

Data shown in the file ”Results_Case D.xlsx” are the results of case D, including the reliability of MMCs with different types of passive redundancy schemes in sheet 1, and the design comparison of MMCs with different passive redundancy types in sheet 2.

Research results based upon these data are pubished at http://doi.org/10.1109/TPWRD.2017.2715664


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Last updated on 2019-23-07 at 09:00