Improved Butterfly Optimization Algorithm for Parameter Identification of Various Photovoltaic Models Including Power Station
Bibliographic record
Abstract
Parameter identification of photovoltaic models (PIPM) is essential for controlling a photovoltaic (PV) system. However, due to its complexity, most existing methods still suffer from problems such as low accuracy, sensitivity to initial values, and local optima. For this, an improved butterfly optimization algorithm (DLBOA) with dimension differential learning is proposed. First, a new adaptive fragrance is introduced to optimize the instability caused by target differences and improve convergence performance. Second, the proposed dimension differential learning strategy improves butterflies’ position by utilizing the excellent dimension information within the population, thereby reinforcing interindividual learning and enhancing population balancing and diversity, ultimately escaping from local optima. Then, after evaluating based on CEC2022, DLBOA identified the parameters for eight models across five materials and outperformed nine state-of-the-art algorithms in terms of accuracy, robustness, and promoting percentage. DLBOA is further compared with nine existing PIPM methods including five numerical methods. Finally, applying DLBOA to the YL PV station in China Guizhou Power Grid under a dynamic climate, multiple metrics confirm DLBOA’s outstanding accuracy, with the reconstructed I-V and P-V curves closely matching synthesized curves. The statistical analysis results demonstrate that the proposed method effectively enhances the robustness of parameter identification while also strengthening the ability to escape local optima, demonstrating the potential to improve PIPM accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".