Condition Monitoring of Underground Power Cables Via Power-Line Modems and Anomaly Detection
Bibliographic record
Abstract
Underground power cables are a widely-deployed class of assets in electric power distribution systems. They are robust in adverse weather conditions and are an aesthetically pleasing alternative to overhead power lines. Unfortunately, the physical observability of underground cables is poor. Current methods for detecting and localizing defects known to progress to failure are cumbersome and expensive, both financially and operationally. In-service cable failures can lead to lengthy power outages and financial losses and can endanger utility staff and the general public. There is thus a need for non-invasive condition monitoring techniques that will allow for early detection of defects likely to progress to a fault, so cost-effective repair or replacement interventions can be planned. In this study, insulation overheating is specifically considered. Thermal damage of XLPE insulation is a prominent damage mechanism for extruded dielectric cable and is thus well studied and understood. A defect detection solution combining powerline modems with semi-supervised anomaly detection for underground cables is proposed. The performance of five well-regarded machine-learning-based anomaly detection algorithms is analyzed through simulation of a real-world 15 kV distribution network. The best-performing algorithm studied had a false alarm rate of under 0.1% with no missed alarms across a range of damage scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".