Compensating for Interface Equipment Limitations to Improve Simulation Accuracy of Real-Time Power Hardware In Loop Simulation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an improved algorithm for power hardware-in-loop (PHIL) simulation that takes the errors introduced by the interface equipment into account. Through modeling and analysis of a PHIL simulation circuit, which is composed of a voltage-source converter and a simple network, the impact of the bandwidth of the interface amplifier and equipment on the PHIL simulation is examined. Based on the analysis, an improved algorithm is proposed that uses additional interface filters (implemented in hardware and/or software) rather than the use of previously attempted compensation techniques. More stable and accurate results can be obtained by using the new algorithm. The validity of the proposed algorithm is verified through a software case study and hardware case studies.
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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 | 0.000 | 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 | 0.001 |
| 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 it