publication . Article . Other literature type . Review . 2019

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios,; Buldain,; Comech,; Gilbert,; Orue,;
Open Access English
  • Published: 27 Jun 2019 Journal: Energies (issn: 1996-1073, Copyright policy)
  • Publisher: MDPI AG
  • Country: Spain
Abstract
<jats:p>This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algo...
Subjects
free text keywords: partial discharges, fault recognition, fault diagnosis, deep neural network, deep learning, machine learning, Technology, T, General Computer Science, computer.software_genre, computer, Control engineering, Deep neural networks, Computation, Artificial intelligence, business.industry, business, Computer data storage, Engineering, Raw data, Data acquisition, Electrical equipment, Partial discharge
Related Organizations
Funded by
EC| MEAN4SG
Project
MEAN4SG
Metrology Excellence Academic Network for Smart Grids
  • Funder: European Commission (EC)
  • Project Code: 676042
  • Funding stream: H2020 | MSCA-ITN-ETN
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publication . Article . Other literature type . Review . 2019

Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios,; Buldain,; Comech,; Gilbert,; Orue,;