基于X射线数字成像的GIS设备缺陷无损检测方法

摘要:

GIS设备的安全性和可靠性对电力体系的平稳运行具有重要意义。因此，为提高对GIS设备缺陷的检测效果、提高设备运行的安全性，在X射线数字成像的基础上，提出一种针对GIS设备的缺陷无损检测方法。通过X射线数字成像的方式采集GIS设备图像，并对图像中存在的泊松噪声实施去噪处理，以提高图像质量。针对处理后的图像，利用二维主成分分析法，通过将复杂的图像数据转换为简单的主成分来表示原始数据，提取出最具代表性的特征。将提取结果输入到BP神经网络分类器中，通过特征分类完成对GIS设备缺陷的无损检测。实验结果表明：应用该方法后，图像识别清晰度较高，对不同类型缺陷的检测效果良好。该方法的优势在于使用先进的图像处理和机器学习技术，能够有效地识别和定位GIS设备中存在的缺陷。通过及时发现并修复这些缺陷，可以提高GIS设备的安全性和可靠性，从而确保电力系统的平稳运行。

Abstract:

The safety and reliability of GIS equipment are of great significance for the smooth operation of the power system. Therefore, in order to improve the detection effect of GIS equipment defects and improve the safety of equipment operation, a non-destructive testing method for GIS equipment defects is proposed based on X-ray digital imaging. Collect GIS equipment images through X-ray digital imaging and denoise the Poisson noise present in the images to improve image quality. For the processed image, two-dimensional principal component analysis is used to represent the original data by converting complex image data into simple principal components, and extract the most representative features. Input the extracted results into a BP neural network classifier and perform non-destructive testing of GIS equipment defects through feature classification. The experimental results show that after applying this method, the image recognition clarity is high and the detection effect for different types of defects is good. The advantage of this method lies in the use of advanced image processing and machine learning techniques, which can effectively identify and locate defects in GIS equipment. By timely discovering and repairing these defects, the safety and reliability of GIS equipment can be improved, thereby ensuring the smooth operation of the power system.