A Dynamic Voltage Regulator compensation scheme for a grid connected village electricity hybrid wind/tidal energy conversion scheme
Bibliographic record
Abstract
Renewable energy can act as a sufficient and economic source of electrical energy in rural villages. In spite of being cheap, clean and abundant, the continuous fluctuations in the renewable energy sources causes significant power quality issues. Although the presence of a weak grid improves the rural village power quality, the presence of a FACTS device can introduce a significant improvement to the power quality of such a network. This paper studies a network presenting a rural load, such as a small village, fed from a wind turbine and a tidal turbine connected to a weak grid. The effect of the variation in wind speed and tides on the power quality is illustrated via simulation. The introduction of the Dynamic Voltage Regulator (DVR) to the network establishes a significant improvement to the power quality. The proposed DVR is a cheap and robust FACTS based device. It is controlled via a tri-loop dynamic error-driven PI controller. This scheme proved its ability to introduce a significant improvement which is illustrated via comparing the simulation results of the studied network with and without the DVR.
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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".