为了准确有效地识别变压器内部的潜伏性机械故障，提出了一种基于时变滤波经验模态分解(TVFEMD)和麻雀搜索算法优化最小二乘支持向量机(SSA-LSSVM)的变压器内部机械故障诊断方法。首先，对铁心处于不同松动状态的变压器进行振动信号采集；其次，利用时变滤波改进的经验模态分解（EMD）对所得振动信号进行分解，以获取多个本征模态函数(IMF)即模态分量；然后，采用相关系数法计算IMF分量与原始振动信号的相关性，并计算相关性最大的IMF分量的样本熵，以此构建特征向量集；最后，以诊断准确率最高为目标函数，利用SSA对LSSVM的正则化参数和核函数参数进行优化，搭建SSA-LSSVM诊断模型，并利用诊断模型对特征向量集进行诊断识别，实现变压器铁心内部潜伏性机械故障的诊断。试验结果表明，所提方法能够有效识别变压器内部潜伏性机械故障，识别准确率达到了98%以上，比对比算法的识别准确率高出5%以上，达到了高识别准确率的诊断效果。

A transformer internal mechanical fault diagnosis method based on time-varying filtered empirical modal decomposition (TVFEMD) and sparrow search algorithm optimized least squares support vector machine (SSA-LSSVM) is proposed to identify the internal latent mechanical faults of transformers accurately and effectively. Firstly, the vibration signals from transformers with iron cores in different loose states are collected. Secondly, the vibration signals are decomposed by the empirical mode decomposition (EMD) which was improved by time-varying filtered to obtain multiple intrinsic mode function (IMF); i.e. modal components (IMFs). Thirdly, the correlation coefficient method was used to calculate the correlation between the IMFs component and the original vibration signal, and the sample entropy of the IMF component with the highest correlation is calculated to construct the feature vector set. Finally, with the highest diagnostic accuracy as the objective function, SSA is used to optimize the regularization parameters and kernel function parameters of LSSVM, the SSA-LSSVM diagnostic model is built. The vector set is diagnosed and identified by using the diagnosis model, and the diagnosis of latent mechanical faults inside the transformer core is realized. The experimental results show that the proposed method can effectively identify the latent mechanical faults inside the transformers, and the identification accuracy reaches more than 98%, which is more than 5% higher than the identification accuracy of the comparison algorithm, achieving the diagnosis effect of high identification accuracy.

国家自然科学基金(51577050); 国网江苏省电力公司重点科技项目(J20200040)