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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 | 2021-06-19T04:35:13Z | - |
dc.date.available | 2021-06-19T04:35:13Z | - |
dc.date.issued | 2021-06-17 | - |
dc.identifier | 4159 | - |
dc.identifier.citation | Wang, Q., Yu, Y., Ahmed, H. O. A., Darwish, M. and Nandi, A. K. (2021) ‘Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method’, Sensors, 21(12), 4159, pp. 1-15. doi: 10.3390/s21124159. | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22865 | - |
dc.description.abstract | Copyright: © 2021 by the authors. Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To classify directly the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/ Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion but it needs more training time. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China; Shaanxi Provincial Science and Technology Agency; Key Laboratory Project of Department of Education of Shaanxi Province | en_US |
dc.format.extent | 1 - 15 (15) | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) 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 | LSTM | en_US |
dc.subject | BiLSTM | en_US |
dc.subject | CNN | en_US |
dc.subject | classification accuracy | en_US |
dc.title | Open-circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) method | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/s21124159 | - |
dc.relation.isPartOf | Sensors | - |
pubs.issue | 12 | - |
pubs.publication-status | Published | - |
pubs.volume | 21 | - |
dc.identifier.eissn | 1424-8220 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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