{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T03:01:13Z","timestamp":1783220473977,"version":"3.54.6"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:00:00Z","timestamp":1596844800000},"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"}]},{"name":"Shaanxi Provincial Key Research and Development Project","award":["2020GY-124"],"award-info":[{"award-number":["2020GY-124"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s20164438","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T05:07:23Z","timestamp":1597036043000},"page":"4438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods"],"prefix":"10.3390","volume":"20","author":[{"given":"Qinghua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"},{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexiao","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"},{"name":"State Grid Sichuan Electric Power Research Institute of China, Chengdu 610094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hosameldin O. A.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Darwish","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6248-2875","authenticated-orcid":false,"given":"Asoke K.","family":"Nandi","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, C., Li, Z., Murphy, D.L.K., Li, Z., Peterchev, A.V., and Goetz, S.M. 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