Modeling of the impulsive noise in the power substation environment and its application to receiver design
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».