Boundary Control for Power Hardware-in-the-Loop Applications
Bibliographic record
Abstract
This work presents a boundary controller suitable for power hardware-in-the-Ioop (PHIL) applications with LCL filters. An LCL filter significantly reduces harmonic distortion, improving the accuracy of PHIL. However, the LCL filter has drawbacks, as it lowers the system bandwidth and introduces an LC resonance. A boundary controller provides a fast dy-namic response and intrinsically handles the LC resonance, but conventional boundary control assumptions break down when the LCL filter has two similar inductances. Modifications to the conventional control law are proposed which handle applications where the two inductances of the LCL filter are similar. This also requires further considerations where the LCL filter is connected to another switching converter. The performance and control accuracy of boundary control is dependent on the sensors, typically requiring high-bandwidth and low-latency sensors. The effect of sensor delays are analyzed, and a compensation method applied. The sensor burden is further reduced through a hybrid analog-digital voltage sensing technique. Control system analysis is provided of the PHIL system with a boundary controller, highlighting the benefits of this approach. The PHIL performance of the conventional algorithm and the modified algorithm are compared to the ideal response. Experimental results compare the PHIL response of the modified algorithm to that of the real load, showing a high accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".