Modelling and Experimental Evaluation of Ideal Transformer Algorithm Interface for Power Hardware in the Loop Architecture
Bibliographic record
Abstract
Interface devices are crucial to achieving Power Hardware in the Loop (PHIL) configuration. It is the interface that separates PHIL implementation with its real counterpart. This inclusion of interface at power decoupling point has raised the concern of stability and accuracy among researchers. It is therefore essential to study the effect of interface if the PHIL system is to be entirely understood. Ideal Transformer Algorithm (ITA) is one of the widely used interfacing method for PHIL due to its implementation simplicity. Moreover, the existing models of ITA relies only on the theoretical model developed. This work constitutes the study and development of accurate mathematical model of individual interface devices in ITA. Further, this paper uses a frequency sweep approach to determine the responses from the actual system consisting of a Real Time Digital Simulator (RTDS). This experimentally obtained frequency response is then compared with model response to test the accuracy of the developed model. This paper therefore bridges the existing gap in interface model by experimentally verifying the developed model. The theoretical and experimental model are well within agreement to further the studies in PHIL.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".