基于图像分割及小波脊线的变压器绕组状态检测  

Transformer Winding Condition Detection Based on Image Segmentation and Wavelet Ridges

作　　者：张淼彬 王丰华[1] 金玉琪 金凌峰 杨智 詹江杨 Zhang Miaobin;Wang Fenghua;Jin Yuqi;Jin Lingfeng;Yang Zhi;Zhan Jiangyang(Department of Electrical Engineering Shanghai Jiaotong University,Shanghai 200240,China;Stage Grid Zhejiang Electric Power Research Institute,Hangzhou 310014,China)

机构地区：[1]上海交通大学电气工程系,上海200240 [2]国网浙江省电力有限公司电力科学研究院,杭州310014

出　　处：《电工技术学报》2025年第2期640-652,共13页Transactions of China Electrotechnical Society

基　　金：国家电网有限公司科技资助项目(5500-202219126A-1-1-ZN)。

摘　　要：新型电力系统的建设给电力设备及电网的安全可靠运行提出了更高的要求,进一步提升了变压器绕组状态的检测水平,该文从变压器振动信号的小波时频图像出发,使用最大类间方差法对其进行图像分割以获取表征绕组状态信息的关键区域,进而利用模极大值法提取经图像分割后各关键区域的小波脊线,据此定义了小波脊线特征向量与特征向量角(WRFVA),对变压器绕组状态进行检测。某110 kV变压器多次短路冲击试验下振动信号的计算结果表明:经图像分割提取出的变压器振动信号小波时频图像的小波脊线时频分辨率高,直观清晰地反映了不同短路冲击电流作用下绕组状态的变化过程;当同一短路电流作用下振动信号的WRFVA的变化超过2°时,意味着绕组有轻微松动或变形存在,建议关注其运行状态。The construction of a new power system poses increasing requirements for the safe and reliable operation of electrical equipment and power grids.As one of the essential pieces in a power system,it is important to effectively detect the winding condition of the transformer with high accuracy.Vibrational signals of an operated transformer always carry abundant information about transformer winding and have served as an important indicator for describing winding conditions.However,the vibration monitoring method has the potential blurry fault patterns in the vibration signals caused by the limitations of the time-frequency methods and the noises from sensors.It is still arduous to accurately identify the transformer’s winding condition under sudden short-circuit currents.This paper introduces the image segmentation technique to analyze the vibration signals of power transformers with the extraction of wavelet ridges.Specially,the continuous wavelet transform is applied to construct the wavelet coefficient modulus matrix.Here,the complex Morlet wavelet function with bandwidth and center frequency of 4 is selected.With the gray treatment of the wavelet coefficient modulus matrix,the maximum inter-class variance method is selected to perform the image segmentation on the wavelet coefficient modulus matrix for the detailed description of the key regions.The second segmentation is further made with the proper selection of the segment threshold.After the element extraction in each region of the wavelet coefficient modulus matrix with the mode maximum method,the wavelet ridge matrix is constructed through the polynomial fitting of the maximum element coordinates.Finally,the wavelet ridge feature vector angle(WRFVA)index is defined to evaluate the condition of the transformer winding.This method can ensure the accuracy and clarity of the obtained wavelet ridges,and the defined WRFVA index can effectively capture the vibration signal variations to judge the winding condition of the transformer.A simulated signal mainly com

关 键 词：变压器 绕组状态 小波脊线 最大类间方差法 图像分割 

分 类 号：TM614[电气工程—电力系统及自动化]