Multi-input single-inductor dc-dc converter for MPPT in parallel-connected photovoltaic applications
Bibliographic record
Abstract
This paper focuses on photovoltaic systems with multiple parallel-connected panels. It is shown that distributed MPPT must be performed on each panel to maintain maximum power harvesting in partial shading conditions. This is especially true for PV systems made with panels having different electrical parameters. The multi-input, single-output (MISO) dc-dc converter provides a low-cost implementation of distributed MPPT for solar applications. A controller with digital peak and valley current control is used to operate the MISO converter in pseudo-CCM mode. A digital input current estimation algorithm based on the inductor current is proposed to iteratively reach DMPPT for each input, while eliminating the need for several current sensors in the system. The overall power benefit from the MISO converter ranges from 7% to 43% in the experimental MISO buck prototype. The proposed low-cost DMPPT solution and control algorithm provide very promising power savings compared to the conventional MPPT approach. The novel solution allows PV systems to be easily expanded without being restricted to panels from a single manufacturer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".