Deep Learning in High Voltage Engineering: A Literature Review

Sara Mantach, Abdulla Lutfi, Hamed Moradi Tavasani, Ahmed Ashraf, Ayman El-Hag and Behzad Kordi
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Sara Mantach: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Abdulla Lutfi: Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Hamed Moradi Tavasani: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Ahmed Ashraf: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Ayman El-Hag: Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Behzad Kordi: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada

Energies, 2022, vol. 15, issue 14, 1-32

Abstract: 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.

Keywords: high voltage apparatus; deep learning; classification; localization; partial discharge; faults; outdoor insulators (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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