Application of Machine Learning in Discharge Classification

Brar, Ramanpreet K.; El-Hag, Ayman H.

doi:10.1109/ceidp49254.2020.9437463

Cited by 6 publications

(1 citation statement)

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“…To the best of our knowledge, there seems to be a gap in the literature that involves training deep learning models to detect internal and external physical defects using radiation-based measurements like RF antenna and ultrasonic sensors. All the work that has been done on radiation-based techniques involves the use of feature extraction and machine learning techniques [122][123][124][125][126].…”

Section: Physical Defect Detectionmentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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Section: Physical Defect Detectionmentioning

confidence: 99%