A design optimisation tool to minimise volume and failure rate of the modular multilevel converter and the thyristor-controlled rectifier
Bibliographic record
Abstract
The 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".