基于ICEEMDAN多尺度模糊熵和MVO-KELM的变压器绕组铁心机械故障诊断

针对变压器绕组铁心机械故障诊断精度不足的问题，提出了一种基于改进自适应白噪声完整集成经验模态分解(ICEEMDAN)多尺度模糊熵(MFE)和多元宇宙优化算法优化核极限学习机(MVO-KELM)的变压器绕组铁心机械故障诊断方法。首先，为了避免虚假模态分量的产生，采用改进的ICEEMDAN对变压器原始振动信号进行分解。其次，利用Pearson相关系数法选取相关性最高的模态分量，并计算其MFE值。然后，将MFE值作为特征量构建特征数据集，并利用MVO优化KELM的核参数和正则化系数。最后，将特征数据集输入所建MVO-KELM模型进行分类识别，实现高准确率诊断目标。试验结果表明，所提方法具有优秀的诊断精度和稳定性，能够精确诊断变压器绕组铁心不同松动程度的故障，诊断准确率达到了99%以上，可为变压器现场检修策略的制定提供一定的指导。

Mechanical Fault Diagnosis for Transformer Winding Core Based on ICEEMDAN Multi-Scale Fuzzy Entropy and MVO-KELM

A transformer winding core mechanical fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) multi-scale fuzzy entropy (MFE) and multi-verse optimizer algorithm optimized kernel based extreme learning machine (MVO-KELM) is proposed for the problem of insufficient accuracy of transformer winding core mechanical fault diagnosis. Firstly, to avoid the generation of spurious modal components, the original transformer vibration signal is decomposed using a modified ICEEMDAN. Secondly, the modal component with the highest correlation is selected using the Pearson correlation coefficient method and its MFE value is calculated. Then, the MFE values are used as feature quantities to construct feature datasets, and the kernel parameters and regularization coefficients of KELM are optimized using MVO. Finally, the feature dataset is input into the constructed MVO-KELM model for classification and identification to achieve the goal of high accuracy diagnosis. The experimental results prove that the proposed approach possesses excellent diagnostic accuracy and stability, and can accurately diagnose transformer winding core loosening faults of different degrees with a diagnostic accuracy of more than 99%, which may supply the necessary guidance for the development of transformer field maintenance strategy.