Low-latency smart grid asset monitoring for load control of energy-efficient buildings
Bibliographic record
Abstract
In the smart grid, demand-side is tightly coupled with the condition of the smart grid assets such as the transformers in a substation, capacitor banks, relays, etc. A fault occurring in any of those assets or an incident causing power quality degradation within a distribution system may trigger load control actions in energy-efficient buildings. In case of such critical conditions, load control actions need to be activated in a timely manner. Therefore, the status of the smart grid assets needs to be monitored in near real-time. Recently, Wireless Sensor Networks (WSNs) have emerged as promising monitoring tools in many fields including military, health and critical infrastructures. However, transmitting delay-critical data in the smart grid via WSNs needs data prioritization and delay-responsiveness. In this paper, we evaluate the performance of two schemes, namely the delay-responsive, cross layer (DRX) data transmission scheme, and the fair and delay-aware cross layer (FDRX) data transmission scheme in various smart grid environments. We consider an outdoor substation, an underground transformer vault and an indoor power room. We show that DRX has lower end-to-end delay than FDRX. On the other hand, delivery ratio of both DRX and FDRX degrades in the outdoor substation when compared to the underground transformer vault. Furthermore, DRX and FDRX are able to satisfy the tight delay requirements of the smart grid.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".