Advanced Smart Grid Monitoring: Intelligent Cable Diagnostics using Neural Networks
Bibliographic record
Abstract
Monitoring and control of network constituents are integral aspects of the smart grid. In this paper, we present a technique for monitoring one such network asset, the underground power cables, which are prone to degradation and damages, resulting in possible power outages. We propose an intelligent cable diagnostics solution using neural networks to determine the health of power cables to predict and prevent eventual faults. To this end, we reuse the communication channel state information inherently estimated by power line modems that are envisioned to enable smart grid communications. We advance the state-of-the-art machine learning based cable health monitoring techniques to present an automated diagnostics procedure using neural networks, which eliminates the need to manually extract features during operation. We demonstrate the architecture of our designed feed-forward neural network, the procedures involved in training, validating, and testing data, and the algorithms we use to train our machines. We evaluate our solution for medium voltage distribution network settings and show through simulation results that our method provides accurate diagnosis in detecting, locating, and assessing cable degradations.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".