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


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  <identifier identifierType="URL">https://zenodo.org/record/3855657</identifier>
  <creators>
    <creator>
      <creatorName>Barrios, Sonia</creatorName>
      <givenName>Sonia</givenName>
      <familyName>Barrios</familyName>
      <affiliation>Ormazabal Corporate Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Buldain, David</creatorName>
      <givenName>David</givenName>
      <familyName>Buldain</familyName>
      <affiliation>University of Zaragoza</affiliation>
    </creator>
    <creator>
      <creatorName>Comech, Maria Paz</creatorName>
      <givenName>Maria Paz</givenName>
      <familyName>Comech</familyName>
      <affiliation>Instituto CIRCE</affiliation>
    </creator>
    <creator>
      <creatorName>Gilbert, Ian</creatorName>
      <givenName>Ian</givenName>
      <familyName>Gilbert</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6343-4936</nameIdentifier>
      <affiliation>Ormazabal Corporate Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Orue, Iñaki</creatorName>
      <givenName>Iñaki</givenName>
      <familyName>Orue</familyName>
      <affiliation>Ormazabal Corporate Technology</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>partial discharges</subject>
    <subject>fault recognition</subject>
    <subject>fault diagnosis</subject>
    <subject>deep neural network</subject>
    <subject>deep learning</subject>
    <subject>machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-06-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3855657</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3390/en12132485</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/oct-cit</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/676042/">676042</awardNumber>
      <awardTitle>Metrology Excellence Academic Network for Smart Grids</awardTitle>
    </fundingReference>
  </fundingReferences>
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