摘要
通过故障电弧实验平台获取大量电弧声数据样本,将电弧声信号进行3层小波包分解,以小波包敏感频带能量作为识别特征量,利用模糊C-均值聚类算法对识别特征量进行模糊聚类,得到真假弧声的模糊分类矩阵和聚类中心,通过计算待测数据样本与已知特征弧声聚类中心的贴近度,实现真假弧声的识别,实验结果表明该方法对故障弧声的正确识别率在91%以上,识别效果良好;最后给出了基于早期特征弧声的故障电弧预测预警方案,改变目前故障电弧的事后被动检测,把故障电弧消除在事故发生之前,降低和避免了故障电弧产生时对开关设备造成的损失。
Faults arc sound sample data caused by different arcing electrodes, space between electrodes, and discharge voltage in switch cabinet were got by fault arc detection system in laboratory. Utilizing these sample data, frequency hand energy features of arc sound signal are extracted based on three--layer wavelet packet decomposition. Faults Arc sound signal recognition scheme based on fuzzy C--mean clustering algorithm was put forward. In this scheme, some characteristic frequency bands energy were used as inputs of fuzzy C--mean clustering algorithm, the optimized classified matrix and clustering centers were obtained. By calculating the closeness rating between the new samples and the trained clustering center,the fault arc sound was identified. Good recognition result is obtained using the scheme. Finally, a forecast and early warning scheme of faults are has been put forward based on the characteristic frequency of are sound, which can change the traditional passive detection method to the active detection method , reduce the damage of switch apparatus by eliminating the arcing faults in the early time.
出处
《计算机测量与控制》
北大核心
2013年第2期532-534,共3页
Computer Measurement &Control
基金
福建省高新技术研究开发计划重点项目(2005H036)
福建省科技计划重大项目(2012H6013)
广西高等学校科研项目(201204LX263)
关键词
开关柜
故障电弧
弧声特征
预测预警
模糊识别
模糊聚类
switch cabinet
fault arc
arc sound signature
forecast and early warning
fuzzy recognition
fuzzy clustering
作者简介
蓝会立（1975-），男，广西马山县人，工学硕士，讲师，主要从事高压电气设备状态监测与故障诊断的研究。
