Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty
Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty
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DOI：
10.1109/tii.2022.3197839
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发表时间：
2023-05
影响因子：
12.3
通讯作者：
Guangya Zhu;Kai Zhou;Lu Lu-Lu;Yao Fu;Zhaogui Liu;Xiaomin Yang
中科院分区：
文献类型：
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作者：
Guangya Zhu;Kai Zhou;Lu Lu-Lu;Yao Fu;Zhaogui Liu;Xiaomin Yang
基于机器学习算法的电力设备局部放电分类，泛化能力不足，识别精度低。针对这一问题，本文提出了一种改进的基于梯度惩罚的Wasserstein生成性对抗网络(WGAN-GP)数据增强模型。改进的WGAN-GP模型可以生成数据样本来补充PD源分类中的低数据输入集。首先训练带条件生成的改进WGAN-GP模型，生成各种新的数据样本。然后，利用新的数据样本来扩展原始数据集。最后，对扩展后的数据集进行训练，得到新的PD分类器。实验结果表明，该模型能够更稳定地生成新的高质量数据样本。此外，该方法还能有效地抑制低数据或数据分布不平衡带来的过拟合风险，有效提高分类精度。
The partial discharge (PD) classification for electric power equipment based on machine learning algorithms often leads to insufficient generalization ability and low recognition accuracy. To solve the problem, this article develops an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based data augmentation model. The improved WGAN-GP model can generate data samples to supplement the low-data input set in PD source classification. First, an improved WGAN-GP model with conditional generation is trained and various new data samples are generated. Then, the new data samples are utilized to expand the raw dataset. Finally, the expanded dataset is trained to get a new PD classifier. Experimental results demonstrate that the proposed model can generate new high-quality data samples more stably. Moreover, the proposed method can suppress the overfitting risk caused by low data or imbalanced data distributions and the classification accuracy is effectively improved.