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Special Issue "Machine Learning for Partial Discharge Monitoring"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (7 July 2021) | Viewed by 508

Special Issue Editors

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: data science; machine learning; AutoML; explainable AI
Special Issues, Collections and Topics in MDPI journals
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK
Interests: data science; signal processing; machine learning; IoT; partial discharge monitoring.
Special Issues, Collections and Topics in MDPI journals
Department of Electronic and Electrical Engineering, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
Interests: machine learning; partial discharge monitoring; wireless technologies; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of machine learning to a diverse range of industrial applications continues rapidly; nowhere is its impact felt more acutely than in condition monitoring of critical national infrastructure. Given the breadth and depth of research in this area, we are delighted to announce a Special Issue on Machine Learning for Partial Discharge Monitoring that will collate the various strands of AI/ML technologies being utilized to combat and mitigate partial discharge.

We welcome original research on machine learning-enabled applications for partial discharge localization (of multiple sources), classification, time-to-failure estimation, and impact assessment. Of particular interest are complementary technologies that include 5G, Edge/Cloud-based solutions, explainable AI (XAI), multi-modal fusion (radio, ultrasound), drone-based platforms, and federated learning strategies.

Dr. Robert C. Atkinson
Prof. Gordon Morison
Dr. Christos Tachtatzis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Partial discharge
  • Machine learning
  • Artificial intelligence
  • Localization
  • Partial discharge classification
  • Condition monitoring
  • Electromagnetic interference
  • Smart grid
  • Electrical substation

Published Papers

There is no accepted submissions to this special issue at this moment.
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