基于SMA-VMD和能量熵的高压断路器故障诊断  

Fault Diagnosis of High-voltage Circuit Breakers Based on SMA-VMD and Energy Entropy

作　　者：范兴明[1] 许洪华 李涛[1] 张鑫[1] FAN Xingming;XU Honghua;LI Tao;ZHANG Xin(Department of Electrical Engineering&Automation,Guilin University of Electronic and Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学电气工程及其自动化系,桂林541004

出　　处：《高电压技术》2024年第12期5248-5258,共11页High Voltage Engineering

基　　金：国家自然科学基金(61741126);广西自然科学基金(2022GXNSFAA035533)。

摘　　要：针对高压断路器机械故障特征难以从分合闸振动信号中有效提取的问题,提出了一种基于优化变分模态分解(variational modal decomposition,VMD)参数和提取能量熵(energy entropy,EE)作为故障特征值的方法。首先,利用黏菌优化算法(sticky mushroom optimization algorithm,SMA)以最小包络熵为适应度函数对VMD算法的参数组合[K,α]进行全局寻优。其次,根据寻优结果设定VMD算法参数对信号进行分解,得到K个固有模态分量(intrinsic modal components,IMF)。然后,计算各模态分量的能量熵,借助相关系数筛选与原始信号较相关的模态分量,构建特征向量并随机划分训练集、测试集。最后,将训练集输入支持向量机(support vector machine,SVM)中训练分类模型,利用已训练的模型对测试集样本进行分类。在1台12 kV高压断路器上进行实验验证,分类结果表明,该文提出的SMA-VMD-EE模型状态识别准确率为95%,相较于VMD-EE、PSO-VMD-EE、SMA-VMD-SE模型的准确率均有所提高,验证了所提方法的有效性和可行性。To solve the problem that it is difficult to effectively extract the mechanical fault features of high-voltage cir-cuit breakers from the tripping vibration signals,we propose a method based on optimizing the parameters of the variational modal decomposition(VMD)and extracting the energy entropy(EE)as the fault eigenvalues.Firstly,the pa-rameter combination[K,α]of the VMD algorithm is globally optimized using the Sticky Mushroom Optimization Algorithm(SMA)with the minimum envelope entropy as the fitness function.Secondly,the parameters of the VMD al-gorithm are set to decompose the signal according to the optimization results to obtain K intrinsic mode function(IMF).Then,the energy entropy of each modal component is calculated,the modal components that are more relevant to the original signal are filtered with the help of the correlation coefficient,and the feature vectors are constructed and random-ly divided into the training set and test set.Finally,the training set is input into the support vector machine(SVM)to train the classification model,and the trained model is used to classify the samples in the test set.Moreover,experimental vali-dation is carried out on a 12 kV high-voltage circuit breaker.The classification results show that the state recognition accuracy of the proposed SMA-VMD-EE model is 95%,which is improved compared with the accuracy of VMD-EE,PSO-VMD-EE,and SMA-VMD-SE models,and the effectiveness and feasibility of the proposed method are verified.

关 键 词：高压断路器 变分模态分解 黏菌优化算法 能量熵 故障诊断 

分 类 号：TM561[电气工程—电器]