基于小波包分解的二次电流回路短接故障自动诊断方法

Automatic Short-Connection Fault Diagnosis Method of Secondary Current Circuit Based on Wavelet Packet Decomposition

当前二次电流回路短接故障自动诊断节点多设定为独立结构,故障诊断识别速度慢,导致小波包分解系数比增大,为此,提出基于小波包分解的二次电流回路短接故障自动诊断方法。采用多目标的方式采集进行异常数据。采用重叠辅助核验的方式实现故障诊断处理。结果表明,所设计的小波包分解二次电流回路短接故障自动诊断测试组最终得出的小波包系数变化比均控制在1.5以下,说明二次电流回路短接故障自动诊断效果更高效,具有实际的应用价值。

The current automatic diagnosis nodes for short-circuit faults in secondary current circuits are mostly set as independent structures,resulting in slow fault diagnosis and recognition speed,leading to an increase in the coefficient ratio of wavelet packet decomposition.Therefore,an automatic diagnosis method for short-circuit faults in secondary current circuits based on wavelet packet decomposition is proposed.Using a multi-objective approach to collect abnormal data.Using overlapping auxiliary verification to achieve fault diagnosis and processing.The results show that the designed wavelet packet decomposition test group for automatic diagnosis of short-circuit faults in the secondary current circuit has a wavelet packet coefficient change ratio controlled below 1.5,indicating that the automatic diagnosis of short-circuit faults in the secondary current circuit is more efficient and has practical application value.