Grid surveillance and diagnostics using power line communications
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
With an aging power distribution infrastructure, it becomes increasingly important for the next generation smart grid to self-monitor its transmission lines. In this paper, we propose a road-map towards achieving a self-reliant grid surveillance using power line communications (PLC). To this end, we exploit the principle that cable faults or degradations manifest themselves as changes in the PLC channel conditions. In particular, by monitoring the channel transfer functions that are already computed in legacy PLC receivers, we enable power line modems with intelligent grid sensing abilities to identify and assess cable anomalies using machine learning techniques. Through simulations, we show that our proposed monitoring and diagnostics solutions successfully empower power line modems to independently detect and predict the extent of water-tree degradations commonly seen in cross-linked polyethylene insulated power cables.
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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