Adaptive direct power control based on ANN-GWO for grid interactive renewable energy systems with an improved synchronization technique
Bibliographic record
Abstract
This paper investigates the improvement of synchronization technique for single-phase inverter. Specifically, the paper proposes a modified structure of second-order generalized integrator with frequency-locked loop (SOGI-FLL) with FLL gain normalization. The proposed structure enhances the frequency detection, which makes it a powerful technique under distorted grid voltage. The validation of the proposed synchronization method includes simulations and experimental tests using Xilinx field programmable gate array (FPGA) as the target device. Moreover, time domain simulations using the direct power control (DPC) with the proposed structure are performed. The decoupled active and reactive powers are controlled using the artificial neural networks (ANNs) trained by the mean of a metaheuristic algorithm. In this paper, the grey wolf optimizer (GWO) is proposed to train the multilayer perceptron (MLP). The proposed approach shows better generation of synchronization signals and smooth power quality, making it suitable for grid-tied and microgrids (MGs) power systems control.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".