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 995
Special Issue Editors
Interests: data science; machine learning; AutoML; explainable AI
Special Issues, Collections and Topics in MDPI journals
Interests: data science; signal processing; machine learning; IoT; partial discharge monitoring.
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. AtkinsonProf. Gordon Morison
Dr. Christos Tachtatzis
Guest Editors
Manuscript Submission Information
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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.
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Keywords
- Partial discharge
- Machine learning
- Artificial intelligence
- Localization
- Partial discharge classification
- Condition monitoring
- Electromagnetic interference
- Smart grid
- Electrical substation
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