Improving Performance of Isolated Fuel Cell Power Conditioners through Series Capacitive Compensation
Bibliographic record
Abstract
In isolated fuel cell power conditioners, the dc output voltage of the fuel cell is first converted to a high-frequency train of positive and negative pulses of equal, but adjustable, widths. The resulting quasi square-wave voltage is then input to a high-frequency step-up isolation transformer, whose secondary voltage is rectified to a regulated dc voltage. This dc voltage, which is supported by batteries and/or ultracapacitors, is finally converted to the desired dc and ac voltages. The equivalent series inductance of the isolation transformer limits the power transfer capability of the system. As a result, the expensive installed capacity of the fuel cell is poorly utilized. Furthermore, the efficiency of power conditioner will be low, as at low power levels, losses are comparable to the total power. In this paper, it is proposed to use a series capacitor, on the secondary-side of the high-frequency transformer, to nullify the equivalent series inductive reactance of the isolation transformer at the switching frequency. It is shown through simulation that this results in considerable improvement in the power transfer capability and efficiency of the system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".