Smart-Grid Monitoring: Enhanced Machine Learning for Cable Diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent works have shown the viability of reusing power line communication modems present in the distribution network for cable diagnostics. By integrating machine learning (ML) techniques, power line modems (PLMs) are shown to be capable of automatically detecting, locating, and assessing different types of cable degradations and faults by monitoring and analyzing their estimated channel frequency responses. However, a single ML algorithm is not ideal for all different diagnostics tasks. To aid us in choosing the most suitable ML algorithm(s) for each of the tasks and to make our solution layman accessible, we propose the use of automated ML, which automatically constructs the best ML model from various algorithms and preprocessing techniques for any given diagnostics task. Our proposed diagnostics approach accelerates the practical deployment of PLM-based grid monitoring by providing a ready-to-use solution to utilities that can be applied without detailed domain knowledge of ML operations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it