A Novel FACTS Based (DDSC) Compensator for Power-Quality Enhancement of L.V. Distribution Feeder with a Dispersed Wind Generator
Bibliographic record
Abstract
In a deregulated electric service environment, an effective electric transmission and distribution networks are vital to the competitive environment of reliable electric service. Power quality (PQ) is an item of steadily increasing concern in power transmission and distribution. The traditional approach to overcoming capacity and quality limitations in power transmission and distribution in many cases is the addition of new transmission and/or generating capacity. This, however, may not be practicable or desirable in the real case, for many of reasons. From technical, economical and environmental points of view, there are two important - and most of the time combined - alternatives for building new transmission or distribution networks to enhance the transmission system capacity, and power quality: the Flexible alternating current transmission devices and controllers, and the distributed generation resources near the load centers. The connection of distributed generation to the distribution grid may influence the stability of the power system, i.e. angle, frequency and voltage stability. It might also have an impact on the protection selectivity, and the frequency and voltage control in the system. This paper presents a low cost FACTS based Dynamic Distribution System Compensator (DDSC) scheme for voltage stabilization and power transfer and quality enhancement of the distribution feeders connected to a dispersed wind generator, using MATLAB/ SimPower System simulation tool.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".