Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Nhat-Quang Dang, Trong-Tai Ho, Tuyet-Doan Vo-Nguyen, Young-Woo Youn, Hyeon-Soo Choi and Yong-Hwa Kim ()
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Nhat-Quang Dang: Department of Computer Science and Information, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea
Trong-Tai Ho: Department of Computer Science and Information, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea
Tuyet-Doan Vo-Nguyen: Department of Electronic Engineering, Myongji University, Yongin-si 17058, Republic of Korea
Young-Woo Youn: Smart Grid Research Division, Korea Electrotechnology Research Institute, Gwangju-si 61751, Republic of Korea
Hyeon-Soo Choi: Genad System, Naju-si 58296, Republic of Korea
Yong-Hwa Kim: Department of Computer Science and Information, Korea National University of Transportation, Uiwang-si 16106, Republic of Korea

Energies, 2023, vol. 17, issue 1, 1-17

Abstract: Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

Keywords: data augmentation; fault diagnosis; phase-resolved partial discharge (PRPD); supervised contrastive learning (SCL) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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