Data Logging and Control of a Remote Inverter Using LoRa and Power Line Communication
Bibliographic record
Abstract
For decades, the power system was highly centralized. With the growing integration of distributed generations into the system, there is a necessity for bi-directional communication methods to monitor and control the remote assets. The primary objective of this paper is to develop a communication link for monitoring and controlling a grid-connected inverter in a remote location. Furthermore, the paper presents developments that have been incorporated to improve the communication link. The literature survey indicates that LoRa is superior compared to other technologies, but has some security and reliability issues. This paper also presents an encryption algorithm to improve the security of the LoRa link. Local data storage added to the system before transmitting data increases the system reliability. A display at the transmission end is added to improve the user-friendliness of the communication link. A Powerline Communication link is parallelly added to the LoRa link to improve the reliability. Finally, tests are conducted with an actual inverter and the results are presented. The tests show that the developed communication link has improved security and reliability, while its open nature makes it highly scalable and adaptable for employment in other smart grid applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".