基于小波和自组织网络的电缆故障识别

为实现电力电缆早期故障在线识别的目的，提出了一种基于小波能量函数和自组织网络的识别方法。首先，提取在线电缆早期故障状态与正常状态的零序电流差的小波能量函数作为输入特征，用自组织神经网络实现故障识别。用欧式距离比较了自组织特征映射神经网络与反向传播（Back Propagation，简称BP）神经网络对电缆故障识别的稳定性。仿真试验结果表明，该识别方法对在线电缆早期故障类型的识别正确可靠，系统具有较好的稳定性。这为电力电缆早期故障的在线诊断提供了理论支持。

Cable Fault Recognition Using Wavelet and Self-Organizing Neural Network

To achieve the online early fault recognition of the power cable, a r ecognition method using the wavelet energy functions and the Self organizing ne u ral network was presented. The difference of the zero order current between the early fault cable and the normal cable was extracted , and then the wavelet ener gy function value of the difference was calculated as the input vector of the se lf organizing neural network which recognized the fault pattern . The early cab l e faults comprised three type faults. The first type was the one phase fault cau sed by the decreasing insulation between one phase and the ground. The second is the two phases fault caused by the decreasing insulations between two phases or between two phases and the ground. The third is the three phases fault caused b y the decreasing insulations between three phases or between three phases and th e ground. Moreover, the recognition stability of the self organizing neural net w ork was compared with the BP neural network by using the Euclidian distance. Fin ally, the simulation experiment shows that the proposed method is correct and re liable and the system is stable.