| 引用本文: | 闵永智,令世文,王 果.基于混合特征选择和IOMA-CNN的变压器故障诊断[J].电力系统保护与控制,2024,52(23):1-9.[点击复制] |
| MIN Yongzhi,LING Shiwen,WANG Guo.Transformer fault diagnosis based on hybrid feature selection and IOMA-CNN[J].Power System Protection and Control,2024,52(23):1-9[点击复制] |
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| 摘要: |
| 为解决变压器油中溶解气体故障特征种类不足和诊断模型准确率较低的问题，提出一种混合特征选择方法。并利用改进光学显微镜优化算法(improved optical microscope algorithm, IOMA)优化卷积神经网络(convolutional neural networks, CNN)，实现变压器故障诊断。首先，基于相关比值法构建30维变压器故障候选特征集，采用混合特征选择方法，通过两次特征选择确定输入集的特征维数。其次，引入Tent混沌映射、自适应t分布变异与动态选择策略改进光学显微镜优化算法(optical microscope algorithm, OMA)，提升其寻优性能。然后，利用IOMA算法对CNN模型的学习率、卷积核大小和数量等超参数进行优化。最后，构建IOMA-CNN变压器故障诊断模型并通过算例分析对其性能进行评估。实验表明，所提方法故障诊断准确率达到98.5%。与常规特征选择方法相比，利用混合特征选择方法所选择的输入特征进行故障诊断能够有效提升诊断准确率。相较于其他优化诊断模型，IOMA-CNN具有更高的准确率和更好的稳定性。 |
| 关键词: 变压器 故障诊断 混合特征选择 光学显微镜优化算法 卷积神经网络 |
| DOI：10.19783/j.cnki.pspc.240153 |
| 投稿时间：2024-01-24修订日期：2024-04-29 |
| 基金项目:国家自然科学基金项目资助(62066024) |
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| Transformer fault diagnosis based on hybrid feature selection and IOMA-CNN |
| MIN Yongzhi,LING Shiwen,WANG Guo |
| (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China) |
| Abstract: |
| There are problems of insufficient types of dissolved gas fault features in transformer oil and a low accuracy of diagnosis. Thus a hybrid feature selection method is proposed. The improved optical microscope algorithm (IOMA) is used to optimize convolutional neural networks (CNN) to realize transformer fault diagnosis. First, a 30 dimensional transformer fault candidate feature set is constructed based on the correlation ratio method, and the hybrid feature selection method is used to determine the feature dimension of the input set through two feature selections. Secondly, a Tent chaotic mapping, adaptive t-distribution mutation and dynamic selection strategy are introduced to improve the optical microscope algorithm (OMA) and enhance its optimization performance. Then, the learning rate, the size and number of convolution kernels of the CNN model are optimized using the IOMA algorithm. Finally, the IOMA-CNN transformer fault diagnosis model is constructed and its performance is evaluated by numerical example analysis. Experiments show that the fault diagnosis accuracy of the proposed method is 98.5%. Compared with the conventional feature selection method, the fault diagnosis accuracy can be effectively improved by using the input features selected by the hybrid feature selection method. Compared with other optimized diagnosis models, IOMA-CNN has higher accuracy and better stability. |
| Key words: transformer fault diagnosis hybrid feature selection optical microscope optimization algorithm convolutional neural network |
