| 引用本文: | 刘建锋,李志远,周亚茹.基于漏磁场和ICOA-ResNet的变压器绕组早期故障诊断[J].电力系统保护与控制,2024,52(9):99-110.[点击复制] |
| LIU Jianfeng,,LI Zhiyuan,ZHOU Yaru.Transformer windings based on leakage field and ICOA-ResNet early fault diagnosis[J].Power System Protection and Control,2024,52(9):99-110[点击复制] |
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| 摘要: |
| 针对变压器绕组变形、轻微匝间短路故障诊断准确率低的问题，提出一种变压器绕组早期故障诊断方法。首先，利用ANSYS仿真软件建立与实验变压器相关参数一致的有限元模型，分析变压器在绕组发生各种故障的漏磁场分布规律，并根据这些规律选取合适的故障特征以及光纤漏磁场传感器安装位置。然后，通过改进长鼻浣熊优化算法(improved coati optimization algorithm, ICOA)寻找残差神经网络(ResNet)的最优超参数，以此参数构建ICOA-ResNet模型，将所得故障特征量输入模型进行故障诊断。最后，通过仿真数据和动模实验验证所提出的变压器绕组早期故障诊断模型的可行性。所提模型与支持向量机等4种模型相比，在绕组早期故障诊断上有更高的准确率，表明所提方法对变压器绕组变形、匝间短路故障诊断的有效性。 |
| 关键词: 变压器早期故障诊断 绕组变形 漏磁场 长鼻浣熊优化算法 残差神经网络 超参数优化 |
| DOI：10.19783/j.cnki.pspc.231278 |
| 投稿时间：2023-09-28修订日期：2024-03-08 |
| 基金项目:国家自然科学基金项目资助(51777119) |
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| Transformer windings based on leakage field and ICOA-ResNet early fault diagnosis |
| LIU Jianfeng,,LI Zhiyuan,ZHOU Yaru |
| (School of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China) |
| Abstract: |
| There is low diagnostic accuracy of transformer winding deformation and slight inter-turn short circuit fault diagnosis. Thus an early fault diagnosis method for transformer windings is proposed. First, ANSYS simulation software is used to establish a finite element model consistent with the relevant parameters of the experimental transformer. It also analyzes the distribution law of the leakage magnetic field of the transformer in the winding faults, and selects the appropriate fault characteristics and the installation position of the optical fiber leakage magnetic field sensor according to these laws. Then, the optimal hyperparameters of residual networks (ResNet) are found by improved coati optimization algorithm (ICOA), and the ICOA-ResNet model is constructed based on these parameters. The fault characteristics are input into the model for fault diagnosis. Finally, the feasibility of the proposed early fault diagnosis model for transformer windings is verified by simulation data and dynamic model experiments. Compared with four other models such as the support vector machine, the proposed model has higher accuracy in early fault diagnosis. This shows that the proposed method is effective in transformer winding deformation and inter-turn short circuit fault diagnosis. |
| Key words: transformer early fault diagnosis winding deformation leakage magnetic field coati optimization algorithm residual networks hyperparameter optimization |
