A Dual-Inductor-Connected Isolated DC–DC Converter With Direct Current Control and Low Current Harmonics
Bibliographic record
Abstract
Renewable energy sources such as fuel cells and photovoltaics (PV) modules are widely installed in recent years. Due to these sources’ characteristics such as low port voltage, current-harmonic-sensitive, and weak short-circuit withstanding capability, their interface converters usually require high voltage gains, low high-frequency (HF) current harmonics, and short-circuit protection. At present, the existing step-up isolated dc–dc converters are usually combined with two capacitor-connected terminals or one capacitor-connected terminal plus one inductor-connected terminal, failing to obtain the low HF current harmonics and convenient direct current control (DCC) on both ports. Hence, a dual-inductor-connected (DIC) isolated dc–dc converter is proposed in this article with the inductor connection on both ports. Then, low HF current harmonics and convenient DCC capability can be realized simultaneously through the connected inductors. Meanwhile, the proposed converter can also achieve voltage spike suppression and soft-switching operation without snubber circuits, and its inherent voltage boost function makes it suitable for high voltage-gain applications. Subsequently, the modulation scheme, topology characters, and control scheme are elaborated in this article. The experimental results based on a scale-down laboratory prototype are presented to verify the steady-state and transient-state performance of the proposed converter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".