A hybrid designed digital dual-loop control of high power ground power unit (GPU)
Bibliographic record
Abstract
400 Hz inverters, known as Ground Power Units (GPUs), are widely used in aviation and marine industry. Due to their approximately eight times higher fundamental frequency, 400 Hz inverters are much more sensitive to practical delays such as sampling delays, when compared to traditional 50-60 Hz inverters. Conventional controllers designed for 50-60 Hz inverters, therefore, must be redesigned to properly address a different sampling rate issue. This paper proposes a modified dual cascade control loop applicable to 400 Hz inverters. The inverter topology has been modified by placing the filter inductor at the input side of transformer. Therefore, in the resulting configuration, the inductor current will serve as a new feedback variable with less harmonic content. The control system employs a proportional and a resonant controller designed in digital and analog domains, respectively, which is a new design approach compared to the existing methods resulting in enhanced performance of the 400 Hz GPU. In addition, a feedforward block has been added to the overall typical dual loop scheme to decouple the control variables from load current disturbances and improve the dynamic response of the inverter. An optimized smooth noise-robust derivative has also been introduced to improve the noise immunity. Simulations and experimental results on a 20 kVA prototype GPU show the validity of the proposed scheme to provide high-performance transient and steady state responses.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".