Smart metering field implementation with power line communication in low voltage distribution grid
Bibliographic record
Abstract
Smart Grids (SG) enable power generation, distribution, transmission, customers and utilities to transfer, predict, monitor and manage energy usage effectively. In order to provide them, SGs need to be fully realized by integrating communication technologies infrastructures. Today, SGs have some main systems. These are Advanced Meter Reading (AMR), distributed renewable energy, energy storage, electric vehicle and smart city-home implementations. AMR is the most prominent SG component between all SG main systems. Because it provides to follow all consumers remotely and momentarily using their Smart Meters (SM). AMR system is based on SM, gateway, meter data management software and wired or wireless communication method. Nowadays, Power Line Communication (PLC) is the most popular wired communication method for remote meter reading because of try to use existing electric distribution grid infrastructure. On the other hand, GPRS/EDGE/3G is one of the widespread wireless communication method for remote meter reading implementations but its costly and external dependence has started the search for new communication methods instead. In this paper, practically twelve electric meters are tried to be read remotely with PLC in Yeşilırmak Electric Distribution Company field until the distribution transformer. These remote meter reading results that captured with PLC will evaluate in AMR software. Then an AMR system model is put forward thanks to this field implementation. Beside this a hybrid communication method has been suggested for smart metering. PLC key parameters will find out. In addition, data concentrator (DC), meter and gateway that has PLC features equipment field configurations will research.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".