Control strategy for improving the power flow between home integrated photovoltaic system, plug-in hybrid electric vehicle and distribution network
Bibliographic record
Abstract
This paper deals on control strategy and analyzing power flow between home integrated photovoltaic system (PVS), plug-in hybrid electric vehicle (PHEV) and distribution network The neutral point clamp (NPC) multilevel inverter is the main element that allows interfacing between the different energy sources and receptors. The combination of synchronous reference frames (SRF) and indirect control algorithms applied to NPC, has allowed the system working in On and Off-grid condition for providing a continuous and uninterruptible power supply, for minimizing losses and managing effectively the power flow. The integrated PVS is supposed to satisfy a power load demand in the normal condition of solar irradiation. A PHEV is charging from PVS or grid and could supply power in case of off-grid emergency situation. An onboard bidirectional charger is modeled and controlled by sliding mode algorithm in order to ensure a secure charging and discharging of PHEV batteries. The system is tested for power factor correction and voltage regulation along with harmonic elimination. The performance of the system is validated using MATLAB software with its Simulink and power system blockset toolboxes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".