| 引用本文: | 张洪嘉,戴志辉,贺欲飞,等.基于条件变分自编码器与改进ConvXGB模型的二次设备小样本故障诊断方法[J].电力系统保护与控制,2026,54(01):156-167. |
| ZHANG Hongjia,DAI Zhihui,HE Yufei,et al.Small-sample fault diagnosis method for secondary equipment based on conditional variational autoencoder and improved ConvXGB model[J].Power System Protection and Control,2026,54(01):156-167 |
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| 摘要: |
| 为解决在二次设备故障诊断过程中小样本数据对模型判别准确率存在较大影响的问题，构建基于条件变分自编码器(conditional variational autoencoder, CVAE)与改进卷积极限梯度提升(convolutional extreme gradient boosting, ConvXGB)模型的二次设备小样本故障诊断方法。首先，梳理二次设备故障类型与故障特征信息，形成故障信息特征集。其次，利用CVAE对特定小样本数据进行数据增强，形成平衡数据集，并通过主成分分析法与t-SNE算法对CVAE的潜在空间进行降维可视化处理。最后，引入自注意力机制，在ConvXGB模型激活函数层后添加压缩与激励网络模块，对数据特征进行重新学习，实现特征权重的自适应分配，完成对故障信息的特征提取与故障类型的诊断。算例分析表明，所提方法在不平衡数据集下，训练集与测试集准确率分别为98.86%和97.75%，能够很好地解决小样本数据的影响，实现二次设备故障类型的快速准确诊断。 |
| 关键词: 二次设备 故障诊断 小样本学习 数据增强 深度学习 |
| DOI：10.19783/j.cnki.pspc.250222 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(51877084) |
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| Small-sample fault diagnosis method for secondary equipment based on conditional variational autoencoder and improved ConvXGB model |
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ZHANG Hongjia, DAI Zhihui, HE Yufei, JIA Wenchao
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Hebei Key Laboratory of Distributed Energy Storage and Microgrid (North China Electric
Power University), Baoding 071003, China
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| Abstract: |
| To address the significant impact of small-sample data on the diagnostic accuracy of models in secondary equipment fault diagnosis, a small-sample fault diagnosis method based on a conditional variational autoencoder (CVAE) and an improved convolutional extreme gradient boosting (ConvXGB) model is proposed. First, fault types and corresponding fault feature information of secondary equipment are systematically analyzed to form a fault information feature set. Second, a CVAE is used to perform data augmentation on specific small-sample datasets, generating a balanced dataset. Principal component analysis and t-SNE algorithm are then used to reduce the dimensionality of the CVAE latent space for visualization. Finally, a self-attention mechanism is introduced by adding a squeeze-and-excitation network module after the activation function layer of the ConvXGB model. This enables re-learning of data features and adaptive allocation of feature weights, thereby completing fault feature extraction and fault type diagnosis. Case studies show that, under unbalanced datasets, the proposed method achieves accuracy of 98.86 % and 97.75 % on the training and test sets, respectively, effectively mitigating the influence of small-sample data and enabling rapid and accurate diagnosis of secondary equipment fault types. |
| Key words: secondary equipment fault diagnosis small-sample learning data augmentation deep learning |
