{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T23:20:23Z","timestamp":1774912823016,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51105291"],"award-info":[{"award-number":["51105291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011710","name":"Shaanxi Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["No. 2020GY-124, No.2019GY-125 and No.2018JQ5127"],"award-info":[{"award-number":["No. 2020GY-124, No.2019GY-125 and No.2018JQ5127"]}],"id":[{"id":"10.13039\/501100011710","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory Project of Department of Education of Shaanxi Province","award":["No.19JS034 and No. 18JS045"],"award-info":[{"award-number":["No.19JS034 and No. 18JS045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify 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.<\/jats:p>","DOI":"10.3390\/s21124159","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T11:20:26Z","timestamp":1623928826000},"page":"4159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"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"],"prefix":"10.3390","volume":"21","author":[{"given":"Qinghua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuexiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Uxbridge UB8 3PH, UK"},{"name":"State Grid Sichuan Electric Power Research Institute of China, Chengdu 610094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8523-1099","authenticated-orcid":false,"given":"Hosameldin O. A.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6248-2875","authenticated-orcid":false,"given":"Asoke K.","family":"Nandi","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4858","DOI":"10.1109\/TPEL.2012.2192752","article-title":"Modeling and control of a modular multilevel converter-based HVDC system under unbalanced grid conditions","volume":"27","author":"Guan","year":"2012","journal-title":"IEEE Trans. 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