Module Efficiency Improvement Method Based on Gray Wolf Optimization Algorithm in Intelligent Management Environment of Photovoltaic Power Plants
Bibliographic record
Abstract
With the gradual depletion of fossil energy resources and the increasingly severe environmental problems, photovoltaic power generation as a typical new energy industry has been highly favored in recent years.In the face of the low efficiency of components often faced by photovoltaic power plants in actual operation, this paper proposes a maximum power point tracking algorithm (IGWO) based on the Gray Wolf optimization algorithm, which optimizes and joins the dynamic weights to expand the search range of the algorithm, and improves the efficiency of solar energy utilization.The gray wolf algorithm is further applied to the optimization of photovoltaic (PV) arrays in power stations, and a PV array reconfiguration algorithm based on the gray wolf optimization algorithm is proposed to randomly generate a radial structure by the broken circle method, and the best reconfiguration scheme is obtained through iterative optimization search.The optimization experiment of photovoltaic power station was carried out, and the photovoltaic array reconstruction algorithm in this paper was used to reconstruct in the static shadow occlusion mode, and the GMPP after reconstruction was significantly improved, and the shadow occlusion mode was increased to 14515.565W,10626.844W,and 10636.467W,respectively, and the tracking accuracy of the IGWO algorithm in this paper also reached 99.9%, 99.5%, and 99.6%, respectively.The tracking accuracy of the IGWO algorithm in this paper for MPPT tracking control is consistently above 99% level under dynamic shadow shading mode.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".