PV module current gains due to structured backsheets
Bibliographic record
Abstract
We evaluate the optical performance of PV modules with respect to an increase in short circuit current density. Our evaluation is based on the combination of ray tracing simulations and measurements on test modules with four types of backsheets: Two of them are structured, the third is white and diffusively reflecting and the fourth reflects no light. Under normal incidence, structured backsheets reflect incoming light at an angle that causes total internal reflection at the glass/air interface, which guides the light to the solar cell surface. Three different irradiance conditions are studied: a) standard testing conditions (STC) with light incident perpendicular to the module surface, b) variation in the angle of incidence and c) light source with mean annual distribution of angles of incidence. Using the measured refractive index data in ray tracing simulations we find a short circuit current density (Jsc) gain of up to 0.9 mA/cm² (2.3%) for monofacial cells and a structured backsheet, when compared to a white backsheet with diffuse reflection. For bifacial cells we calculate an even larger Jsc increase of 1.4 mA/cm² (3.6%). The Jsc increase is larger for bifacial cells, since some light is transmitted through the cells and thus more light interacts with the backsheet. Our optical loss analysis reveals the best performance in STC for edge-aligned Ag grooves. This structure reduces absorption losses from 1.8 mA/cm² to 0.3 mA/cm² and reflection losses from 0.7 mA/cm² to 0 mA/cm². This trend also holds under various angles of incidence as confirmed consistently by Jsc measurements and ray racing simulations. Simulations using an annual light source emitting a mean annual distribution of angles of incidence reveal grooves in both orientations edge alignment and east-west alignment achieve similar current gains of up to 1.5% for mono- and of 2.5% for bifacial cells compared to modules with white back sheets. This indicates that for modules with light guiding structures such as these backsheets optimization for STC differs from optimization for annul yield.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".