Computational Intelligence-Based Modeling of a UAV-Integrated PV Module in Icing Conditions
Bibliographic record
Abstract
Solar UAVs utilize solar energy to extend flight endurance and reduce maintenance compared to traditional UAVs. However, in-flight icing presents significant challenges, impacting both aerodynamic performance and the operational reliability of UAV-integrated photovoltaic (PV) systems. Ice accumulation on wings degrades mechanical properties, while icing on PV modules obstructs sunlight, adversely affecting their parameters. Partial shading from ice is more harmful than uniform shading, complicating PV module behavior analysis. This study presents a novel approach to model the behavior of UAV-integrated PV modules incorporating the impact of nonuniform in-flight icing into irradiance calculations. By analyzing how ice formation acts as an obstacle and considering its effect on the PV module governing equations, this research develops a computational framework alongside with segmenting the PV module's operational curve into two zones: ice-covered and normal. The parameters for these zones are determined using advanced computational intelligence methods. The proposed method enables predictions of PV module performance using trained machine learning models, enhanced by the minimum redundancy maximum relevance technique, under dynamic and adverse conditions such as movement-induced sunlight variations and partial shading. The trained models’ performance is validated through experimental tests, demonstrating the reliability and effectiveness of the approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".