基于短时傅里叶变换和稀疏表示的局放识别分类方法

Method for identifying and classifying partial discharge based on short time Fourier transform and sparse representation

文中采集了开关柜中不同放电类型的局部放电超声波信号,使用短时傅里叶变换获得超声波信号的时频图,使用稀疏表示算法对时频图进行分类,能够快速准确地判断出是否发生放电,并判断属于以下哪种放电类型:球板放电、柱板放电、锥板放电、针板放电。在使用稀疏表示方法过程中,分别采用了正交匹配追踪法和加速近端梯度法两种方法进行稀疏求解,通过实验说明了这两种方法适用于不同的情况。实验证明该方法相比于传统多分类支持向量机方法具有更高的准确率、鲁棒性和稳定性,并且更适合解决局部放电识别和分类问题。

In this paper,the different types of partial discharge ultrasonic signals in the switchgear are collected,the time frequency graph of the ultrasonic signal is obtained by short time Fourier transform,and the time frequency graph is classified by the sparse representation algorithm,which can quickly and accurately determine whether the discharge occurs,and determine which of the following discharge types,such as sphere plate discharge,cylinder plate discharge,cone plate discharge and needle plate discharge.In the process of using sparse representation method,two methods of orthogonal matching pursuit and accelerating neighbor gradient method are used to solve the sparse solution.The experiments show that the two methods are suitable for different situations.Compared with the traditional multi-classification support vector machine(SVM)method,it has higher accuracy,robustness and stability.It is proved that the sparse representation method is more suitable to solve the problem of partial discharge recognition and classification.