Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN
Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/487 |
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| author | Xin Du Jun Qi Jiyi Kang Zezhong Sun Chunxin Wang Jun Xie |
| author_facet | Xin Du Jun Qi Jiyi Kang Zezhong Sun Chunxin Wang Jun Xie |
| author_sort | Xin Du |
| collection | DOAJ |
| description | Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy. |
| format | Article |
| id | doaj-art-0d8e752da28b48fe8ee131743e8c47d2 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-0d8e752da28b48fe8ee131743e8c47d22024-11-26T17:45:22ZengMDPI AGAlgorithms1999-48932024-11-01171148710.3390/a17110487Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GANXin Du0Jun Qi1Jiyi Kang2Zezhong Sun3Chunxin Wang4Jun Xie5Alxa Power Supply Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Alxa 750300, ChinaAlxa Power Supply Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Alxa 750300, ChinaAlxa Power Supply Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Alxa 750300, ChinaState Key Laboratory of New Energy Power System, North China Electric Power University, Baoding 071000, ChinaState Key Laboratory of New Energy Power System, North China Electric Power University, Baoding 071000, ChinaState Key Laboratory of New Energy Power System, North China Electric Power University, Baoding 071000, ChinaAccurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.https://www.mdpi.com/1999-4893/17/11/487partial dischargedata augmentationDAEGANDAE-GAN |
| spellingShingle | Xin Du Jun Qi Jiyi Kang Zezhong Sun Chunxin Wang Jun Xie Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN Algorithms partial discharge data augmentation DAE GAN DAE-GAN |
| title | Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN |
| title_full | Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN |
| title_fullStr | Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN |
| title_full_unstemmed | Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN |
| title_short | Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN |
| title_sort | partial discharge data augmentation and pattern recognition method based on dae gan |
| topic | partial discharge data augmentation DAE GAN DAE-GAN |
| url | https://www.mdpi.com/1999-4893/17/11/487 |
| work_keys_str_mv | AT xindu partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan AT junqi partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan AT jiyikang partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan AT zezhongsun partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan AT chunxinwang partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan AT junxie partialdischargedataaugmentationandpatternrecognitionmethodbasedondaegan |