改进的小波包-特征熵在高压断路器故障诊断中的应用

Fault Diagnosis for High Voltage Circuit Breakers With Improved Characteristic Entropy of Wavelet Packet

在详细介绍小波包与特征熵的基础上,将二者结合提出了一种诊断高压断路器机械故障的新方法,并给出了切实可行的诊断步骤和分析。该方法首先将断路器基座振动信号进行3层小波包分解,提取第3层各节点重构信号的包络;然后利用正常状态标准信号所得各包络信号的等能量分段方式,实现对应节点待测状态信号包络的时间轴分段,并利用各分段积分能量、按照熵理论提取特征熵向量;最后构造简单的BP神经网络实现特征熵向量的分类。经正常和2种故障状态下高压断路器无负载振动信号测试,证明该方法检测高压断路器故障简单、准确,为断路器的故障诊断开拓了新的思路。

Based on the introduction of wavelet packet .and characteristic entropy, a new method to diagnosis fault for high voltage circuit breakers is presented, and its steps and analysis are also introduced. The method combines the strongpoint of wavelet packet and characteristic entropy. Firstly, vibration after clearing up noise is wp-decomposed at the third level, and the eight signals of each junction at the third level .are reconstructed; Secondly, the vector is extracted with the segmental energy of reconstructed signals based on the theory of entropy; and lastly the classification of characteristic parameter is realized with simple BP neural network for fault diagnosis. The experimentation without loads indicates the method can easily and accurately diagnose breaker faults, and exploit a new road for fault diagnosis of HV circuit breakers.