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http://bura.brunel.ac.uk/handle/2438/22557
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DC Field | Value | Language |
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dc.contributor.author | Wang, Q | - |
dc.contributor.author | Yu, Y | - |
dc.contributor.author | Ahmed, HOA | - |
dc.contributor.author | Darwish, M | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2021-04-19T15:14:11Z | - |
dc.date.available | 2021-04-19T15:14:11Z | - |
dc.date.issued | 2020-08-08 | - |
dc.identifier.citation | Wang, Q., Yu, Y., Ahmed, H.O.A., Darwish, M. and Nandi, A.K. (2020) 'Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods', Sensors, 20 (16), 4438, pp. 1 - 19. doi: 10.3390/s20164438. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22557 | - |
dc.description.abstract | © 2020 by the authors. In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges’ currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection. The two-terminal MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN, the SoftMax classifier performed better in detection and classification accuracy as well as testing speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less training time than the AE-based DNN and SoftMax classifier | en_US |
dc.description.sponsorship | National Natural Science Foundation of China, grant number No. 51105291; Shaanxi Provincial Science and Technology Agency, No. 2020GY-124. | en_US |
dc.format.extent | 1 - 19 | - |
dc.format.medium | Electronic | - |
dc.language | en | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/. | - |
dc.subject | MMC-HVDC | en_US |
dc.subject | fault detection | en_US |
dc.subject | fault classification | en_US |
dc.subject | CNN | en_US |
dc.subject | AE-based DNN | en_US |
dc.subject | SoftMax classifier | en_US |
dc.subject | classification accuracy | en_US |
dc.subject | speed | en_US |
dc.title | Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/s20164438 | - |
dc.relation.isPartOf | Sensors | - |
pubs.issue | 16 | - |
pubs.publication-status | Published online | - |
pubs.volume | 20 | - |
dc.identifier.eissn | 1424-8220 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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