A FACTS Based Hybrid Filter Compensator (HFC) for H2V Battery Charging Schemes
Bibliographic record
Abstract
The paper presents a robust low impact FACTS based filter compensation scheme for V2H Battery chargers to, improve the power quality, reduce total harmonic distortion, decrease AC and DC inrush currents, and ensure effective Ac and DC Common bus voltage stabilization. The Neutral point Facts Filter Compensation Schemer (NP-HFC) ensures effective decoupling of the AC-DC Sides and minimal impact of inrush currents during fast charging modes. In the same time, the novel FACTS device ensures efficient energy utilzation and improved power factor at the common AC bus. A dynamic multi regulation multi-loop error driven control strategy is developed to ensure fast charging, minimal impact on host electric grid and efficient utilization of grid-connected battery charging scheme with effective AC-DC decoupling and stabilization of the DC Common Bus Voltage. The self regulating battery charging multi- regulator control scheme has been fully validated using Matlab-Simulink Software Environment. The FACTS-based Battery Charging V2H unit is controlled using modified multi-zonal error driven control strategies for fast dynamic action and minimal stead state error to ensure improved power factor operation, reduced Total Harmonic Distortion and decoupled AC- DC Grid Operation. The battery charger has a hybrid selected Voltage-Current Regulation strategy.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".