LoRa-based communication system for data transfer in microgrids
Bibliographic record
Abstract
This paper proposes a LoRa-based wireless communication system for data transfer in microgrids. The proposed system allows connection of multiple sensors to the LoRa transceivers, and enables data collection from various units within a microgrid. The proposed system focuses on communications at the secondary communication level of the microgrid between local controllers of each distributed generation (DG) unit and the microgrid central controller due to the possibility of applying low-bandwidth communication systems at this level. In a proof of concept test bed setup, the data collected by the sensors are sent to the LoRa gateway, which serves as the central monitoring system from which control messages are sent to various microgrid components through their local controllers such as DG units, storage systems and load. In this work, to improve communication security, a private server has been developed using Node-Red instead of cloud servers that are currently used in most Internet-of-Things (IoT) monitoring systems. A range test of the proposed system is carried out to observe the rate of data delivery. It demonstrated over 90% data delivery at 4 km. Finally, a test bed experiment is conducted to validate key features of the proposed system by achieving one-directional data transfer in a grid monitoring system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".