Machine Learning Studies for PD Monitoring

Partial discharges (PDs) are crucial to CMD (Condition Monitoring and Diagnosis) of high voltage and high field electrical insulation of power apparatuses and power electronics. Recent development of machine learning (ML) methods shall enable to accelerate such PD studies. There are lots of ML algorithms available to select. ML classification is more useful to investigate PD monitoring rather than ML regression. A recent review is given for this contemporary topic in the paper.

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Published
2024-08-04
DOI
https://doi.org/10.1109/icpadm61663.2024.10750593
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
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article

Machine Learning Studies for PD Monitoring

Toshikatsu Tanaka
Air Quality Monitoring and Forecasting
article

Machine Learning Studies for PD Monitoring

Toshikatsu Tanaka
article en

Abstract

Partial discharges (PDs) are crucial to CMD (Condition Monitoring and Diagnosis) of high voltage and high field electrical insulation of power apparatuses and power electronics. Recent development of machine learning (ML) methods shall enable to accelerate such PD studies. There are lots of ML algorithms available to select. ML classification is more useful to investigate PD monitoring rather than ML regression. A recent review is given for this contemporary topic in the paper.

Waseda University (JP)
Openalex Percentile: Top 82%
Air Quality Monitoring and Forecasting
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Machine Learning Studies for PD Monitoring — Toshikatsu Tanaka · (2024) | TGRS Research Map | TGRS