Journal article Open Access

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

Barrios, Sonia; Buldain, David; Comech, Maria Paz; Gilbert, Ian; Orue, Iñaki


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Barrios, Sonia</dc:creator>
  <dc:creator>Buldain, David</dc:creator>
  <dc:creator>Comech, Maria Paz</dc:creator>
  <dc:creator>Gilbert, Ian</dc:creator>
  <dc:creator>Orue, Iñaki</dc:creator>
  <dc:date>2019-06-27</dc:date>
  <dc:description>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) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.</dc:description>
  <dc:identifier>https://zenodo.org/record/3855657</dc:identifier>
  <dc:identifier>10.3390/en12132485</dc:identifier>
  <dc:identifier>oai:zenodo.org:3855657</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/676042/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/oct-cit</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>partial discharges</dc:subject>
  <dc:subject>fault recognition</dc:subject>
  <dc:subject>fault diagnosis</dc:subject>
  <dc:subject>deep neural network</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:title>Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
14
13
views
downloads
Views 14
Downloads 13
Data volume 45.9 MB
Unique views 12
Unique downloads 11

Share

Cite as