Modeling of the impulsive noise in the power substation environment and its application to receiver design
Bibliographic record
Abstract
Networks of Intelligent Electronic Devices (IEDs) can be deployed in existing substations by electricity providers, such as Hydro-Qubec, in order to control and monitor power equipment remotely.Such applications are important tenets of the so-called Smart Grid.Wireless technologies can be used in these IED networks, however, the electromagnetic environment within substations is characterized by Radio Frequency (RF) noise that is significant enough to disturb existing wireless technologies.We study the RF environment of substations in the Industrial Scientific and Medical (ISM) band that contains most of wireless carrier frequencies: 780 MHz -2.5 GHz.The main purpose of this thesis work is to design a wireless system that is robust against the substation RF noise.To reach such a goal, we must gather enough information about substation RF noise and we must design a noise model that can represent the substation environment; thereafter we are able to design a robust receiver that is adapted to substation noise.According to the literature and our previous experiments, substations RF noise is mainly composed of an AWGN background noise that is randomly "switched" to impulses with a damped oscillating waveform.One call this noise impulsive noise.In this thesis work, we have designed our own measurement setup to record sequences of impulsive noise samples in the ISM band of interest.The setup can measure substation impulsive noise, in wide band, with enough samples per time window and enough precision to perform a statistical study of the noise.During our measurement campaign, we have recorded around 120 noise sequences in different substations and for four ranges of equipment voltage, which are 25 kV, 230 kV, 315 kV and 735 kV.From the measurement campaign, we know all the characteristics of substation impulsive noise regarding the substation equipment voltage and we have provided representative parameters for the four voltage ranges and for several existing impulsive noise models.Substation impulsive noise is composed of correlated impulses, which requires models with memory in order to replicate a similar correlation.Among different models, we have configured a Partitioned Markov Chain (PMC) with 19 states (one state for the background noise and 18 states for the impulse); this Markov-Gaussian model is able to generate impulsive noise with correlated impulse samples.The correlation is observable on the impulse duration and the power spectrum of the impulses and our PMC model provides characteristics that are more similar to the characteristics of substation impulsive noise in comparison
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".