A Semi-Supervised Approach for Partial Discharge Recognition Combining Graph Convolutional Network and Virtual Adversarial Training

Yi Zhang (), Yang Yu, Yingying Zhang, Zehuan Liu and Mingjia Zhang
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Yi Zhang: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, China
Yang Yu: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, China
Yingying Zhang: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, China
Zehuan Liu: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102200, China
Mingjia Zhang: State Grid Zhejiang Electric Power Co., Ltd. Construction Company, Hangzhou 310009, China

Energies, 2024, vol. 17, issue 18, 1-15

Abstract: With the digital transformation of the grid, partial discharge (PD) recognition using deep learning (DL) and big data has become essential for intelligent transformer upgrades. However, labeling on-site PD data poses challenges, even necessitating the removal of covers for internal examination, which makes it difficult to train DL models. To reduce the reliance of DL models on labeled PD data, this study proposes a semi-supervised approach for PD fault recognition by combining the graph convolutional network (GCN) and virtual adversarial training (VAT). The approach introduces a novel PD graph signal to effectively utilize phase-resolved partial discharge (PRPD) information by integrating numerical data and region correlations of PRPD. Then, GCN autonomously extracts features from PD graph signals and identifies fault types, while VAT learns from unlabeled PD samples and improves the robustness during training. The approach is validated using test and on-site data. The results show that the approach significantly reduces the demand for labeled samples and that its PD recognition rates have increased by 6.14% to 14.72% compared with traditional approaches, which helps to reduce the time and labor costs of manually labeling on-site PD faults.

Keywords: partial discharge (PD); unlabeled PD samples; semi-supervised learning; graph convolutional network (GCN); virtual adversarial training (VAT) (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: 2024
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