基于改进EEMD的高压断路器振声联合故障诊断方法
高压断路器是电力系统中关键的控制和保护设备，针对其故障诊断方法的不足之处，将振声数据级融合和特征级融合应用于高压断路器故障诊断方法。振声特征级融合诊断方法首先将采集到的声波信号通过快速核独立分量分析（Fast KICA）实现盲源分离处理，其次利用改进集合经验模式分解（EEMD）提取振动信号和声波信号的特征向量。振声数据级融合诊断方法首先构建振声联合图像，其次利用改进的BEEMD提取特征向量。最后将两种方法提取的特征向量输入支持向量机模型（SVM）进行故障诊断，实验结果表明，所提方法诊断高压断路器故障能取得良好的效果。...
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| Published in | 电力系统保护与控制 Vol. 42; no. 8; pp. 77 - 81 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
华北电力大学保定校区电气工程学院,河北 保定,071003%云南电网公司电力研究院,云南 昆明,650217
2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
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| Summary: | 高压断路器是电力系统中关键的控制和保护设备，针对其故障诊断方法的不足之处，将振声数据级融合和特征级融合应用于高压断路器故障诊断方法。振声特征级融合诊断方法首先将采集到的声波信号通过快速核独立分量分析（Fast KICA）实现盲源分离处理，其次利用改进集合经验模式分解（EEMD）提取振动信号和声波信号的特征向量。振声数据级融合诊断方法首先构建振声联合图像，其次利用改进的BEEMD提取特征向量。最后将两种方法提取的特征向量输入支持向量机模型（SVM）进行故障诊断，实验结果表明，所提方法诊断高压断路器故障能取得良好的效果。 |
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| Bibliography: | High voltage circuit breaker is the key of control and protection equipment in power system, in allusion to the deficiency of the fault diagnosis methods, this paper boosts the vibration and acoustic data level fusion and feature level fusion method used in high voltage circuit breaker fault diagnosis. The vibration and acoustic feature level fusion diagnosis method firstly makes acoustic signals collected achieve blind source separation processing through fast kernel independent component analysis （Fast KICA）, and extracts the vibration signal and acoustic signal feature vector by the improved ensemble empirical mode decomposition （EEMD）. The vibration and acoustic data level fusion diagnosis method firstly builds vibration acoustic joint image, and extracts feature vector by the improved BEEMD. Finally, the feature vector extracted by the two methods are input into support vector machine （SVM） for fault diagnosis. The experiment shows that the proposed method is effective to diagnose the faults of high volt |
| ISSN: | 1674-3415 |