Deep Learning in High Voltage Engineering: A Literature Review

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...

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Bibliographic Details
Published in:Energies (Basel) 2022-07, Vol.15 (14), p.5005
Main Authors: Mantach, Sara, Lutfi, Abdulla, Moradi Tavasani, Hamed, Ashraf, Ahmed, El-Hag, Ayman, Kordi, Behzad
Format: Article
Language:English
Subjects:
Algorithms
Automation
Cables
Classification
Contamination
Deep learning
Electric power
Electric power systems
Fault detection
faults
High voltage
high voltage apparatus
High voltages
Insulators
Learning algorithms
Literature reviews
localization
Machine learning
Neural networks
partial discharge
Voltage
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15145005