{"id":7373,"date":"2026-05-27T18:42:27","date_gmt":"2026-05-27T10:42:27","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7373"},"modified":"2026-05-27T20:27:10","modified_gmt":"2026-05-27T12:27:10","slug":"jase-202609-32-058","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-058","title":{"rendered":"Generative Diagnosis of Partial Discharge Fault Types in High-Voltage Cables Using Sparse Data Augmentation"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Ren Peng<\/i><\/a>, Song Jun, Ji Hongwei, Ren Peng, Yang Yong, and Chen Jie<\/p>\n\n\n\n

Shandong Luruan Digital Technology Co., LTD. No. 2008, Xinluo Street, High-tech Industrial Development Zone, Jinan City,
Shandong Province, Yinhe Building, China<\/p>\n<\/div>\n\n\n\n

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Received: December 24, 2025
Accepted: April 1, 2026
Publication Date: May 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall Architecture of BAGAN Model <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.058<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Partial discharge (PD) in high-voltage cables often includes rare defect types, while field collected pulse phase analysis (PRPD) spectra typically exhibit sparsity and class imbalance. Limited representative samples and weakly discriminative features hinder accurate extraction of deep time-frequency characteristics, leading to misclassification of defect types. To address this issue, this study proposes a generative diagnostic framework based on sparse data augmentation. An attention-enhanced BAGAN model is applied to augment sparse PRPD spectra, producing a balanced dataset across defect categories. The enhanced dataset is then processed using VMD-MSE to extract low-redundancy, high-discriminative time-frequency features. These features are subsequently input into an IKHA-optimized Deep Belief Network (IKHA-DBN) for defect classification. Experimental results show that generative augmentation increases rare defect samples by 700%, effectively eliminating class imbalance. The synthesized PRPD spectra exhibit strong consistency with genuine spectra in phase distribution and amplitude characteristics, confirming physical plausibility. In testing on 64 multi-class samples, only one misclassification occurred, demonstrating high diagnostic accuracy and sensitivity to minority defects. The results validate the robustness and effectiveness of the proposed method for intelligent diagnosis of rare PD defects in high voltage cables.<\/p>\n\n\n\n

Keywords: Sparse Data Augmentation, High-Voltage Cable, Partial Discharge, Defect Type Diagnosis, Generative Adversarial Network<\/em><\/p>\n\n\n\n

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