Masterless Interleaving Scheme for Parallel-Connected Inverters Operating with Variable Frequency Hysteretic Current-Mode Control
Bibliographic record
Abstract
This paper describes a new masterless interleaving scheme for parallel sub-inverters operating in variable frequency Hysteretic Current-Mode Control (HCMC). The proposed digital control scheme enables the development of fault-tolerant, modular, efficient, and power dense inverters. A feed-forward controller is used to instantaneously correct for phase error at each switching cycle, while a compensator corrects for the systematic difference in sub-inverter switching frequencies caused by inductor mismatch. The control is verified by simulation up to 2 kW in an inverter system consisting of three parallel sub-inverters. Experimental results are presented for the same system operating in Boundary Conduction Mode (BCM) at 895 W. Activation of the interleaving controller shows a reduction in peak current into the system EMI filter, while maintaining the soft-switching benefits of BCM operation. The proposed interleaving scheme reduces the magnitude of the lowest switching frequency harmonics by a peak of 35 dB and an average of approximately 10 dB, which is demonstrated by comparing the inverter system's current spectrum with and without interleaving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".