Quantifying Edge Brightening and Current Mismatch Loss in Fixed-Tilt and Single-Axis Tracked Bifacial PV Systems
Bibliographic record
Abstract
Common photovoltaic (PV) performance modelling software approximate energy yield using a central irradiance sampling line within an infinite row of modules. In such an approach, user-defined corrective factors must represent the irradiance non-uniformity across a row of bifacial PV modules. To contribute to a growing understanding of the upper bound for corrective factors in utility scale systems, this study quantifies the net effect of edge brightening gains and current mismatch losses for central rows in a 2-in-portrait fixed-tilt and single-axis tracked (SAT) PV test-bed at 55°N. Using the DUET PV energy yield software, this study demonstrates optical and electrical calculations for 200,000+ sample points across the row, including two-dimensional non-uniformity profiles for the front and rear of each row in each system. Rear edge brightening ranging from 18.3-32.7 W/m2 contributes to a 0.3-0.5% annual edge brightening gain in energy yield counteracted by 0.1-0.2% current mismatch losses, depending on the system. A method combining the per-timestamp current-voltage curves of all 88 modules in the row demonstrates +0.3% and +0.25% net energy yield gain for fixed-tilt and single-axis tracked systems, respectively, as compared to a central module approximation. Any model that does not inherently capture irradiance non-uniformity along a row of modules should thus allow a net positive corrective factor.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".