Optimized Data Acquisition Point Placement for an Advanced Metering Infrastructure Based on Power Line Communication Technology
Bibliographic record
Abstract
Different communication technologies have been suggested for developing the smart grid communication network. Among these communication technologies, power line communication (PLC) has widely been used, as it has a large coverage range and can access remote areas using existing infrastructures. In this paper, we derive a mathematical model for devising an advanced metering infrastructure (AMI) in the distribution grid based on PLC technology. In order to collect the traffic from thousands of smart meters, intermediary data collectors are placed on selected distribution transformers. However, an optimized placement of data collectors is necessary in order to meet the strict latency requirements needed for time-critical traffic from the meters. For this, we first formulate the latency based on the medium access characteristics of the powerline intelligent metering evolution standard. We then propose an optimization platform for efficiently placing data collectors in such a way that the reliability requirement for the smart grid traffic is ensured and also the installation cost is minimized. We apply the devised optimization solution to realistic examples of AMIs, and we show the effectiveness of our approach through numerical performance evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".