摘要
由于传统的变压器绕组绝缘缺陷检测方法的检测精度较低,影响了电力系统稳定运行,因此提出了基于机器视觉的网络变压器绕组绝缘缺陷检测系统。设计了NJG1144KA1低噪声增幅器与XC7Z100微处理器,初步提升了系统检测精准度。构建了网络变压器绕组绝缘缺陷检测系统软件框架,设计了软件整体结构;再利用机器视觉,设计了绕组绝缘缺陷检测算法,进一步提升检测精准度,进而实现了绕组绝缘缺陷的精准检测。采用系统测试的方式,验证了该系统的检测精准度更高,能保障电力系统的运行稳定性,极具推广价值。
Because of the low detection accuracy of the traditional transformer winding insulation defect detection method,which affects the stable operation of the power system,a network transformer winding insulation defect detection system based on machine vision is designed.The NJG1144KA1 low noise amplifier and XC7Z100 microprocessor are designed to initially improve the detection accuracy of the system.The software framework of network transformer winding insulation defect detection system is constructed,and the overall structure of the software is designed.Then,the machine vision is used to design the winding insulation defect detection algorithm,which further improves the detection accuracy and realizes the accurate detection of winding insulation defects.Through system testing,it is verified that the detection accuracy of the system is higher,which can guarantee the operation stability of the power system,and has great promotion value.
作者
张玲
ZHANG Ling(Schneider Electric Information Technology(Xiamen)Co.,Ltd.,Xiamen 361000,China)
出处
《电工技术》
2023年第7期64-66,71,共4页
Electric Engineering
关键词
机器视觉
网络变压器
绕组
绝缘缺陷检测
machine vision
network transformer
winding
insulation defect detection
作者简介
张玲(1984-),硕士,研究方向为电子器件,工厂运营管理、组织优化、人才梯队建设等。
