Power Loss Investigation of Switch Configurations Using Wide Bandgap Devices in 10kW Current Source Inverters for Solar Applications
Bibliographic record
Abstract
With the steady expansion of renewable energy comes the need to develop next-generation power converters focusing on high power density, efficiency, and reliability with lowered costs, simple structure, and the ability to meet strict grid codes. The current source inverter (CSI) is an interesting topology for photovoltaic (PV) energy applications due to its natural boosting capability, increased reliability through the reduction in DC-link capacitor size, and inherent short circuit protection. However, CSIs suffer from significant conduction losses due to the need for reverse voltage blocking semiconductors and a large DC-link inductor with high losses. With the rollout of commercially available wide band gap (WBG) devices, these drawbacks can be mitigated. Therefore, this paper explores the loss distribution and efficiency of six switch configurations based on conventional Si and WBG solutions through PSIM thermal module simulations. The method used to size the DC-link inductor is discussed, and equations for computing the losses are reviewed. The optimum switching frequency range for each configuration is determined. Finally, the loss distribution at different ambient temperatures is discussed.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".