An Adaptive Moment Estimation-Based Sine Cosine Algorithm With Historical Updates for Robust Second Harmonic Current Suppression
Bibliographic record
Abstract
Second harmonic current (SHC) suppression has become an essential focus in advancing modern power supply systems. This article presents an adaptive moment estimation-based sine cosine algorithm (AdSCA) with historical updates for augmented SHC suppression in power supply systems. Specifically, a tailored second harmonic current compensator (SHCC) is designed to mitigate SHC, with stability analyzed in a dual closed-loop control framework. Besides, the AdSCA is employed to optimize the control parameters of SHCC by multilevel nested optimization of integrating global exploration capabilities of the sine cosine algorithm (SCA) with the local development of adaptive moment estimation (Adam), further enhanced by historical best updates to improve iteration efficiency. The proposed algorithm ensures robust performance against electrical parameter variations, considerable computation performance and guarantees both satisfactory steady-state and dynamic performance for the power supply system. Finally, experimental results are provided and compared with multiple algorithms, demonstrating the effectiveness and robustness of the proposed AdSCA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".