| 严灵潇,李斌,舒嘉辉,张勇志.基于融合迁移学习的IPCNN串联型故障电弧检测研究[J].电子测量与仪器学报,2025,39(11):258-272 |
| 基于融合迁移学习的IPCNN串联型故障电弧检测研究 |
| Research on IPCNN series fault arc detection based on fusion transfer learning |
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| DOI： |
| 中文关键词: 格拉姆角场 IPCNN 改进人工旅鼠算法 迁移学习 故障检测 |
| 英文关键词:Gram-angle IPCNN improved artificial lemming algorithm transfer learning fault diagnosis |
| 基金项目:国家自然科学基金(51674136)、2024年辽宁省教育厅基本科研项目（LJ232410147055）资助 |
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| Author | Institution |
| Yan Lingxiao | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Li Bin | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Shu Jiahui | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
| Zhang Yongzhi | Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China |
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| 中文摘要: |
| 针对实际家庭环境中，家用负载故障数据难以采集导致故障样本稀缺，无法满足故障模型的训练要求的问题。提出了一种融合迁移学习的改进双通道卷积神经网络（IPCNN）串联型故障电弧检测方法。首先，搭建家用负载串联型电弧故障实验平台，获取感性负载和阻性负载在发生串联故障时的一维电压信号，利用格拉姆角场将其转换为二维图像，形成新的图片数据集并将其送入源域上的双通道卷积神经网络（PCNN）模型中进行训练得到该模型的权重参数。然后通过迁移学习将源域上已训练好的权重参数迁移至目标域上的IPCNN模型中，加快模型训练时间，节省计算资源。同时，在IPCNN模型中加入了门控循环单元（GRU）和多头注意力机制（MSA）来提高模型计算效率和表达能力，并且在IPCNN模型中舍弃掉PCNN模型中的分类层，使用L2正则化支持向量机（L2-SVM）代替Softmax层进行分类任务控制模型的复杂度，从而提高模型的泛化能力。最后，针对模型中的学习率和神经元个数等超参数难以确定的问题，利用改进后的人工旅鼠算法进行优化，使其网络结构更加合理。通过对比实验，该模型对感性负载和阻性负载的平均识别准确率分别为97%和97.75%。证明所提方法克服了在数据稀缺的情况下导致模型识别精度低的问题，对于家用负载串联电弧故障的识别具有良好的成效。 |
| 英文摘要: |
| In the actual home environment, it is difficult to collect fault data for household loads, resulting in the scarcity of fault samples and the inability to meet the training requirements of the fault model. In this paper, an IPCNN series fault arc detection method based on transfer learning was proposed. Firstly, an experimental platform for series arc faults of household loads was built to obtain the one-dimensional voltage signals of inductive loads and resistive loads in series faults, and converted them into two-dimensional images by using the Gragram angle field to form a new image dataset and send it to the PCNN model on the source domain for training to obtain the weight parameters of the model. Then, the trained weight parameters on the source domain are migrated to the IPCNN model on the target domain through transfer learning, which accelerates the model training time and saves computing resources. At the same time, GRU and MSA are added to the IPCNN model to improve the computational efficiency and expressive ability of the model, and the classification layer in the PCNN model is discarded, and the L2-SVM is used instead of the Softmax layer to control the complexity of the classification task in the IPCNN model, so as to improve the generalization ability of the model. Finally, in order to solve the problem that the learning rate and the number of neurons in the model are difficult to determine, the improved artificial lemming algorithm is used to optimize the network structure more reasonable. Through comparative experiments, the average recognition accuracy of the model for inductive and resistive loads is 97% and 97.75%, respectively. It is proved that the proposed method overcomes the problem of low model recognition accuracy in the case of data scarcity, and has good results in the identification of series arc faults of household loads. |
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