FBG Sensing Technology for an Enhanced Microgrid Performance
Bibliographic record
Abstract
Energy provided by microgrids should be considered, especially because their purpose is to supply loads from the available power source of the combined sources of energy, including the grid, optimally and efficiently to satisfy the load demand securely and economically. Sensing the accuracy of the different physical parameters of the combined power sources and energy storage plays a crucial part in the efficiency and resilience of microgrids. The present microgrids mostly use conventional sensors, which are greatly impacted by ambient conditions such as high-voltage (HV) and electromagnetic interference (EMI). So, this paper presents an enhanced microgrid based on replacing the conventional sensors with fiber Bragg grating (FBG) sensors renowned for their immunity to EMI and HV, in addition to the virtue of distributing sensing capability. The enhanced microgrid based on FBG sensing was tested experimentally at different potential points predefined on the microgrid and validated with a microgrid simulation model. Real-time measurements of FBG and conventional sensors were recorded at the potential points and applied to the Simulink model to compare the performance for both cases. The unit and integration tests showed an obvious improvement in the accuracy and resiliency of the microgrid by using FBG sensors.
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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 | 0.000 |
| 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 | 0.000 |
| 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".