Numerically Efficient and Accurate Analytical Converter Semiconductor Loss Calculation for Hybrid and Modular Multilevel Converters in VSC-HVDC Applications
Bibliographic record
Abstract
This paper outlines a method enabling the quick and accurate calculation of semiconductor conduction and switching losses of multilevel voltage-sourced converters. The proposed method needs only the equations defining the voltages and currents of the converter’s stacks of submodules and director switch valves to calculate the overall converter semiconductor losses, thereby accelerating the design cycle of novel converter topologies. For any defined operating point, the method quickly returns the semiconductor losses, making it straightforward to sweep across a converter’s range of operation, enabling quick comparison with other well-known, state-of-the-art converters. The method is derived for any generic multilevel converter, while examples of its application to the hybrid three-level converter, which is composed of both stacks and director switches, validate its accuracy. The method is further applied to the extended-overlap alternate-arm converter, also composed of stacks and director switches, providing further evidence of the method’s consistency. To validate the results, the semiconductor losses obtained from detailed simulations of a 600kV, 1GVA VSC-HVDC converter test system are compared against the proposed method, which demonstrate exceptional agreement. The relative errors in overall semiconductor losses between the simulation and the proposed method for the H3LC and EO-AAC are 0.76% and 0.94%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".