摘要
弹簧操动机构作为高压断路器(High voltage circuit breakers,HVCBs)分合闸操作的储能单元,其可靠性对电力系统的安全运行具有重要意义。本文以六氟化硫高压断路器的弹簧操动机构为研究对象,分析分合闸弹簧的动作机理,对弹簧进行不同程度的故障设置。介绍了振动、声音传感器设备及采集参数,针对小波包时频分析法的缺点,提出一种基于小波包-灰度共生矩阵(Gray level co-occurrence matrix,GLCM)的特征提取方法。从诊断速度和诊断准确度两方面对比了支持向量机(Support vector machine,SVM)、决策树(Decision tree,DT)、朴素贝叶斯、K近邻(K nearest neighbors,KNN)4种诊断模型。实验结果表明,在模拟实际应用场景中,选用K近邻算法对分合闸弹簧故障进行深度诊断能够准确判断故障类型及故障程度,对高压断路器安全可靠运行具有实际应用价值。
As an energy storage unit for the opening and closing operations of high-voltage circuit breakers(HVCBs),the reliability of the spring operating mechanism is of great significance to the safe operation of the power system.In this paper,the spring operating mechanism of the SF6 HVCB is the research object,the action mechanism of the opening and closing spring is analyzed,and the mechanical failure of the spring is simulated.The vibration and sound sensor equipment and acquisition parameters are introduced.Aiming at the shortcomings of wavelet packet time-frequency analysis,a feature extraction method based on wavelet packet-gray level co-occurrence matrix(GLCM)is proposed.Then,the four diagnostic models of support vector machine(SVM),decision tree(DT),naive Bayes,and K nearest neighbors(KNN)were compared in terms of diagnosis speed and diagnosis accuracy.The experimental results demonstrate that in the simulation actual application scenario,the KNN algorithm is selected to perform an in-depth diagnosis of the opening and closing spring faults,which can accurately determine the type and degree of the fault,and has practical application value for the safe and reliable operation of high-voltage circuit breakers.
作者
张艳飞
邵阳
公维炜
张昭维
武建文
ZHANG Yanfei;SHAO Yang;GONG Weiwei;ZHANG Zhaowei;WU Jianwen(Inner Mongolia Power Research Institute,Hohhot 010020,China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China)
出处
《机械科学与技术》
CSCD
北大核心
2024年第2期274-281,共8页
Mechanical Science and Technology for Aerospace Engineering
基金
内蒙古电力集团(有限)责任公司科技项目资助(内电科技〔2021〕3号)。
