An Isolated Modular Multiport Converter for the Integration of Photovoltaic Energy Sources and Battery Storage in MVDC Networks
Bibliographic record
Abstract
The intermittent nature of solar photovoltaic (PV) energy sources necessitates the use of energy storage devices, such as batteries, in electrical networks. Typically, each energy resource is integrated into the network through a separate power conversion stage. That is, clusters of PV arrays and batteries are each connected to the grid via separate power conversion stages. This paper, therefore, proposes a novel converter topology based on the dual active bridge (DAB) and modular multilevel converter (MMC) topologies that is capable of integrating both PV arrays and batteries into a medium-voltage dc (MVdc) network through a single stage. Moreover, the converter operates with a modified modulation scheme that reduces the current stress on its transformer in the presence of non-unity dc link voltage ratios. It is also shown that the converter can harvest energy from the PV arrays with maximum-power-point tracking (MPPT) over a wide range of dc voltages. All of the developed mathematical models are verified through simulation studies carried out in theMatlab& Simulink software environment. The results of the simulation are further verified by a 600-W experimental prototype.
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 | 0.000 |
| 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".