RF characterization of substations: Parameters for impulsive noise models based on the equipment voltage
Bibliographic record
Abstract
Installing wireless Intelligent Electronic Devices (IED) for Substation Automation (SA) requires a thorough study of the electromagnetic radiations coming from the power equipment. In our previous work, we have performed a measurement campaign within several substations working under different voltages and we have recorded around 120 sequences of impulsive noise samples in the 700 MHz-2.5 GHz band. In this paper, we present a method to classify substation impulsive noise in order to characterize a representative Radio Frequency (RF) environment of substations for specific substation voltages. The main contribution of this work is to provide representative impulsive noise characteristics in order to calculate parameters for impulsive noise models and to improve the characterization of substation RF noise. To reach this objective, we classify impulsive noise characteristics, such as the impulse amplitude, the impulse duration and the repetition rate for substations under 25 kv, 230 kV, 315 kV and 735 kV. By using the impulsive noise characteristics, we estimate representative parameters for two impulsive noise models: the Middleton class-A (MCA) and the Bernoulli-Gaussian with memory (BGM).
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".