Thermal Management of Flat Photovolatic Panels using Serrated Fins to Increase Electrical Output
Bibliographic record
Abstract
The electrical power output of photovoltaic (PV) cell depends on its operating temperature during its absorption of solar radiation and conversion of solar energy to electrical energy. The increase in PV panel temperature due to overheating negates its electrical yield and efficiency. In addition, overheating causes hot spots, failure of adhesive seals and delamination. An effective way to combat this problem is to reduce the operating temperature of PV panel by cooling. In the present work, a novel thermal management technique for improved cooling of flat PV panel is proposed with the use of serrated fins rather than straight fins. For this reason, the thermal and electrical performance of the flat PV panel with cooling system consisting of duct, brushless DC cooling fan, a plate fin and serrated fins of varying angles (30o, 45o and 60o) made up of aluminium were investigated experimentally. Experiments were conducted at constant wind velocity (1 m/s) with the developed technique in the location of Tiruchirappalli (78.6 E to 10.8 N), Tamil Nadu, India with flat 10 W PV panel. By using serrated fins of varying angles of 30o, 45o, 60o and plate fin (90°), the temperature of the PV panel decreased by a maximum of 4oC, 7oC, 6oC and 3oC respectively. Similarly the PV power increased in the range of 15.38%, 61.53%, 41.53% and 7.69% for 30o, 45o, 60o and plate fin (90°) respectively. It is concluded that 45o angled serrated fin is more efficient in providing the cooling effect than the other angles of serrated fins considered.
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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 | 0.000 |
| 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".