基于小波和深度学习的配电网单相接地故障辨识

随着配电网规模的不断扩大,发生单相接地故障后产生的危害也愈加严重,为避免故障进一步升级,必须迅速采取措施切除故障。配电网故障辨识有利于快速查明故障原因,进而采取相应措施切除故障。同时,故障辨识也是故障选线的前提。针对上述情况,文中介绍了一种利用小波分析提取故障特征量并用深度神经网络进行故障辨识的方法。结果表明,该方法可对小电流接地系统各类单相接地故障进行辨识且辨识准确率高,而且辨识精度受噪声污染影响比传统人工神经网络小。

Identification of single-phase grounding fault in distribution network based on wavelet and deep Learning

As the scale of the distribution network continues to expand,the hazard caused by the occurrence of single-phase ground faults is also significantly increased.In order to avoid further escalation of the fault,measures must be taken quickly to remove the fault.The distribution network fault identification is helpful to quickly identify the cause of the fault and take corresponding measures to remove the fault.Meanwhile,fault identification is also a prerequisite for fault line selection.In view of the above situation,this paper proposes a new method for fault identification through using deep neural networks.The results show that the method can identify various single-phase ground faults of small current grounding systems and the identification accuracy is high,and the identification accuracy is less affected by noise pollution than the traditional artificial neural networks.