DEVELOPMENT OF COST-EFFECTIVE FAULT LOCALIZATION PLATFORM FOR UNDERGROUND CABLE SYSTEM
Notice bibliographique
Résumé
The mining industry is highly dependent on electricity or other forms of energy converted into electricity. Here, one of the common forms of electricity distribution is through underground cables, but the electricity systems regularly confront contingencies due to cable breakdowns or defects. Depending on fault types, detective tools, and cable position, it may take several hours to days to detect the exact fault location with existing methods and tools, which affects the entire mining operation with financial losses and safety issues. Besides, industrial fault localization platforms are very expensive and seldom tailored for the mining industry. To locate such faults, the online fault localization platform has drawn wide attention but is limited in functionalities and applications. This research aims to develop a cost-effective double-ended online fault localization platform while proposing solutions for data synchronization and accurate traveling wave arrival time detection problems. At first, extensive market research has been done on available platforms to access those platforms’ functionalities and costings. Then, a hardware setup has been developed by combining appropriate sensors, a data acquisition unit, a computational platform, and other necessary components. Furthermore, to establish communication between remote platforms, a Python-based software program has been developed. Besides, query-based server management has been introduced to handle and manage huge amounts of data. Combining the hardware and software, the overall cost of a single platform is CAD $ 2,850.00, which is at least ten times less than the least expensive market option. The chosen double-ended traveling wave-based online fault localization method requires accurate synchronized time to properly compute the traveling wave arrival time difference. Thus, coordinated universal time alignment of acquired time series data is needed. Global Positioning System (GPS) based universal time synchronization is one of the popular ways. There are several other techniques, but all of these have some practical difficulties and dependencies. To solve this problem, a cost-effective novel zero-crossing point-based data synchronization approach has been proposed. This approach doesn’t rely on GPS receivers or any other existing methods; rather, the measurement is synchronized by calibrating the zero-crossing points of the sinusoid measurements before the fault. In this way, appropriate synchronization has been realized. The accuracy of traveling wave-based fault localization highly depends on how accurately its arrival times are detected from ends of cable. Accurate detections are achieved when signals sampling rates are high. Usually, a data acquisition system with a higher sampling rate may address the issue, but it increases cost. On the other hand, available interpolation-based up-sampling techniques have several constraints. Machine learning model can solve this problem if trained by real inputs and outputs with desired sampling rates for different types of faults. In this research, a machine learning-based up-sampling model has been presented, which improves the accuracy of traveling wave arrival time detection. Besides, it is cost-effective and outperforms interpolation techniques. Therefore, the proposed fault localization platform combines all the above solutions, which makes the platform very advance, reliable, and cost-effective.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».