Comparative Analysis of Partial Discharge Pattern Recognition Using Deep Learning and Machine Learning

Tae Yun Hong, Hyun Mo Ahn, Hyun Jae Jang, Jun Kyu Park, Jong Ho Sun, Jin Gyu Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Partial Discharge (PD) defect type analysis is important for evaluating insulation performance. A machine learning feature extraction algorithm is presented for AC PD pattern data collected in the laboratory, along with deep learning algorithms for PD pattern images and PD time series data. In addition, data is collected under conditions different from those used for artificial intelligence (AI) training, and algorithm performance is evaluated and compared.

Original languageEnglish
Title of host publication2024 10th International Conference on Condition Monitoring and Diagnosis, CMD 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages702-704
Number of pages3
ISBN (Electronic)9788986510225
DOIs
StatePublished - 2024
Event10th International Conference on Condition Monitoring and Diagnosis, CMD 2024 - Gangneung, Korea, Republic of
Duration: 20 Oct 202424 Oct 2024

Publication series

Name2024 10th International Conference on Condition Monitoring and Diagnosis, CMD 2024

Conference

Conference10th International Conference on Condition Monitoring and Diagnosis, CMD 2024
Country/TerritoryKorea, Republic of
CityGangneung
Period20/10/2424/10/24

Keywords

  • deep learning
  • high voltage
  • machine learning
  • partial discharge
  • phase resolved partial discharge

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