Power Line Communication Based Smart Grid Asset Monitoring Using Time Series Forecasting
Bibliographic record
Abstract
Monitoring grid assets continuously is critical in ensuring the reliable operation of the electricity grid system and improving its resilience in case of a defect. In light of several asset monitoring techniques in use, power line communication (PLC) enables a low-cost cable diagnostics solution by re-using smart grid data communication modems to also infer the cable health using the inherently estimated communication channel state information. Traditional PLC-based cable diagnostics solutions are dependent on prior knowledge of the cable type, network topology, and/or characteristics of the anomalies. In contrast, we develop an asset monitoring technique in this paper that can detect various types of anomalies in the grid without any prior domain knowledge. To this end, we design a solution that first uses time-series forecasting to predict the PLC channel state information at any given point in time based on its historical data. Under the assumption that the prediction error follows a Gaussian distribution, we then perform chi-squared statistical test to determine the significance level of the resultant Mahalanobis distance to build our anomaly detector. We demonstrate the effectiveness and universality of our solution via evaluations conducted using both synthetic and real-world data extracted from low- and medium-voltage distribution networks.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".