Development of a more flexible low-level control for MMC applications
Bibliographic record
Abstract
With the increasing amount of MMC (Modular Multilevel Converter) applications, the need for low-level control algorithms is also increasing. Most low-level algorithms still belong to the FPGA platform. Therefore, unless one is experienced in developing and implementing on a particular FPGA platform, addressing the need for a low-level control algorithm can be extremely difficult. In addition, the cost associated with the development environment and a testing FPGA hardware platform makes it even more difficult to develop such algorithm. This paper introduces a more flexible method of low-level controller development by using a real-time digital simulator (RTDS) as a platform. This allows the developed low-level control model to be interfaced with an FPGA based MMC simulation in the same real-time digital simulator. The flexibility and usefulness in the proposed method is shown in a comparison of the sorting algorithms in NLC(Nearest Level Control) type low-level MMC control.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".