Power Line Communications for Low-Voltage Power Grid Tomography
Bibliographic record
Abstract
Power line communications (PLC) has attracted considerable attention for supporting smart grid applications. Since it reuses the existing grid infrastructure, it offers cost advantages over alternative communications methods and gives electric utilities control over the communications medium. Furthermore, the "through-the-grid" property of PLC extends its possible use beyond mere communications. Since the PLC signals are bound to travel through the power grid, they can also be used for inference tasks, such as online diagnostics of power line integrity. In this paper, we consider such an inference application of PLC, enabled by modern signal processing. We assume a power grid at whose edges PLC devices are deployed to form a PLC network for purposes such as advanced meter reading. We are interested in retrieving the physical power-grid topology, i.e., the connections and lengths of power lines reaching to the locations of the PLC devices. To this end, we propose the combination of PLC-based ranging with inference based on end-to-end measurements. In the context of communication networks, the latter is known as tomography and hence, we refer to the developed method as power grid tomography. For the purpose of ranging we formulate a new super-resolution ranging algorithm specifically tailored for signal propagation through power lines. Numerical results for low-voltage distribution grid examples demonstrate the successful reconstruction of the grid topology by the proposed power grid tomography method.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".