Optimizing Grid Stability and Power Quality through the UPQC- FLC-EVA Integration in Renewable Energy System
Bibliographic record
Abstract
This study aims to enhance the efficiency of a grid-connected system incorporated with renewable energy sources (solar and wind energy sources), electric vehicles and energy storage systems. Traditional research focuses on individual components, whereas this study employs advanced techniques for improving power quality, such as unified power flow controller, generalized UPFC, static VAR compensator, and artificial intelligence -based methods like fuzzy logic controller with UPFC and unified power quality conditioner with FLC. We propose an EV aggregator to optimize power flow and enhance power utilization. The FLC ensures optimal power management during peak and off-peak loads, while UPQC addresses load-side power quality issues. Results indicate that the FLC-based maximum power point tracking outperforms the ANN-based approach, achieving superior power output from photo voltaic and wind energy systems. The study concludes that the synergistic combination of FLC and UPQC ensures efficient power utilization, with electric vehicle contributing significantly to grid stability and flexibility.
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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 | 0.000 | 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) | 0.000 | 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".