基于CEEMDAN-GAF的变压器机械故障检测方法

Mechanical faults detection method for transformers based on CEEMDAN-GAF

针对基于振动信号的传统变压器机械故障检测方法中需要选择多个特征量这一缺点,文中介绍了一种基于自适应白噪声完备集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与格拉姆角场(gramian angular field,GAF)的配电变压器机械故障判别方法。所提方法利用CEEMDAN对信号进行重构并应用GAF变换获得重构信号的二维图像,通过对二维图像灰度处理、二值化后将所得二值矩阵用于训练径向基函数(radical basis function,RBF)神经网络,实现对于机械故障的检测。选用一台变压器进行了故障模拟及测试,结果表明该方法准确有效。工程实际中通过持续大量采集变压器运行数据优化RBF神经网络的分类函数,可以实现不同类型故障的精准识别,具有较高的参考价值。

Aiming at the shortcomings of traditional mechanical fault detection methods based on vibration signals that multiple feature quantities need to be selected,this paper introduces a mechanical fault identification method for distribution transformer based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and Gramian angular field(GAF).This method uses CEEMDAN to reconstruct the signal and applies GAF transformation to obtain a two-dimensional image of the reconstructed signal.After the two-dimensional image is gray-scaled and binarized,the resulting binary matrix is used to train the radial basis function(RBF)neural network to realize the detection of mechanical faults.A transformer was used to simulate and test the fault,and the results show that the proposed method is accurate and effective.In engineering practice,the classification function of the RBF neural network can be optimized by continuously collecting a large number of transformer operating data,which can realize the accurate identification of different types of faults,and has high reference value.