A design optimisation tool to minimise volume and failure rate of the modular multilevel converter and the thyristor-controlled rectifier
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Bibliographic record
Abstract
AbstractThe unfolding of MVDC (Medium Voltage DC) systems has the prospects to enable the incorporation of power electronic converters with higher power density and reliability. A tool with an integrated design approach is required to minimise the overall system volume by identifying optimal components. In this paper, a component-level early-stage design tool has been developed to attain the minimum achievable volume and failure rate for MVDC power converters. The developed tool optimises the choice of semiconductor switching devices, required heatsink, and other passive components (including dc-link filters and inductors) to minimise failure rate and overall converter volume. The optimisation algorithm employs the non-dominated sorting genetic algorithm (NSGA-II) to evaluate designs based on developed fitness functions. The design tool demonstrates the trade-off when evaluating multiple converter topologies and helps make an informed decision. A comparative study between two converter topologies shows the outcomes in terms of targeted metrics (volume and failure rate). This tool is expected to benefit early-stage design to perform trade-off studies among power electronic converter topologies based on key metrics like volume and failure rate.Keywords: All-electric shipfailure ratemodular multilevel convertermedium voltage DCmulti-objective optimisationvolume optimisation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially sponsored by the U.S. Office of Naval Research under contract N00014-16-1-2956.Notes on contributorsTanvir Ahmed ToshonTanvir Ahmed Toshon received the Master's degree in electrical engineering from the Florida State University in 2017. Currently, he is pursuing the PhD degree in electrical engineering with Florida State University, Tallahassee, FL, USA. He joined the Center for Advanced Power Systems, Florida State University, in 2015, where he worked as a Research Assistant. His current research interests include MVDC systems, power electronics, and distribution systems. He has been a part of more than 10 technical papers during his research at CAPS.M. O. FaruqueMd. Omar Faruque (Senior Member, IEEE) received the Ph.D. degree from the University of Alberta, Canada, in 2008. Since 2008, he has been working at the Center for Advanced Power Systems at Florida State University. In 2013, he was also appointed as a Faculty with the FAMU-FSU, College of Engineering. He is currently an Associate Professor with the FAMU-FSU, College of Engineering, Florida State University. He has published more than 100 publications, including 40 in peer-reviewed journals. His research interests include modeling and simulation, smart grid and renewable energy integration, energy management and demand response, and ship power system design. He is a member of other IEEE Task Forces and Working Groups. He has been serving as the Chair for the IEEE Power and Energy Society (PES) Task Force on “Real-Time Simulation of Power and Energy System” since 2012. He is also a Co-Chair of the working group “Modeling and Analysis of System Transient Using Digital Programs.” He also served as a Guest Editor for many special issues of IEEE, IET, and Energies journals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it