Application of Narrow Band Power Line Communication in Volt/Var Optimization
Bibliographic record
Abstract
Smart grid is an advanced and sophisticated electrical network that uses a combination of information and communication technology to gather required data from different nodes on the network through a reliable, robust and cost effective communication solution, and acts on them to improve the efficiency and reliability of production and distribution of electricity.Volt/Var optimization (VVO) is one of the most important smart grid applications that gives this capability to utility companies to have an efficient electricity distribution network by maintaining an acceptable voltage level along the distribution section under different loading situations.This project focused on characterizing and evaluating the performance of narrow band power line communication (NB-PLC) as a prevalent communication technology for smart grid applications such as VVO in North American power grid.This work was done by establishing and setting up a real test bed in the lab.In this work we tried to implement S-parameter measurements due to its simplicity for channel characterization at high frequency ranges, when we have cascades of different components over the channel.It should be mentioned that the main focus of our work was on analyzing the behavior of an energized 5KVA MV/LV transformer over our channel.Then, we have shown the relationship between S-parameters and ABCD parameters to derive the channel transfer function based on ABCD matrix.Additionally, we did some simple noise measurements over our channel when it was non-energized and energized.
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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 | 0.001 |
| 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) | 0.002 | 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".