@inproceedings{36610dd16af24890b8dd84db6efb68b2,
title = "Comparative Analysis of Partial Discharge Pattern Recognition Using Deep Learning and Machine Learning",
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.",
keywords = "deep learning, high voltage, machine learning, partial discharge, phase resolved partial discharge",
author = "Hong, {Tae Yun} and Ahn, {Hyun Mo} and Jang, {Hyun Jae} and Park, {Jun Kyu} and Sun, {Jong Ho} and Kim, {Jin Gyu}",
note = "Publisher Copyright: {\textcopyright} 2024 The Korean Institute of Electrical Engineers (KIEE).; 10th International Conference on Condition Monitoring and Diagnosis, CMD 2024 ; Conference date: 20-10-2024 Through 24-10-2024",
year = "2024",
doi = "10.23919/CMD62064.2024.10766190",
language = "English",
series = "2024 10th International Conference on Condition Monitoring and Diagnosis, CMD 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "702--704",
booktitle = "2024 10th International Conference on Condition Monitoring and Diagnosis, CMD 2024",
address = "United States",
}