CAJ | 학술논문

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。
고압단로기시전력계통중관건적공제화보호설비，침대기고장진단방법적불족지처，장진성수거급융합화특정급융합응용우고압단로기고장진단방법。진성특정급융합진단방법수선장채집도적성파신호통과쾌속핵독립분량분석（Fast KICA）실현맹원분리처리，기차이용개진집합경험모식분해（EEMD）제취진동신호화성파신호적특정향량。진성수거급융합진단방법수선구건진성연합도상，기차이용개진적BEEMD제취특정향량。최후장량충방법제취적특정향량수입지지향량궤모형(SVM)진행고장진단，실험결과표명，소제방법진단고압단로기고장능취득량호적효과。
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 voltage circuit breakers.