Abstract Traditional analysis of cable early state recognition is mainly based on one or several threshold values of electrical characteristics, but the calculation of threshold is often affected by measurement accuracy and external disturbance, which inevitably reduces recognition accuracy. The development of artificial intelligence provides a new way to solve this problem. This paper presents a deep convolutional neural network (CNN) recognition method for early state of 10 kV single core cable based on sheath current. Firstly, waveform and energy characteristics which are extracted from the mutation information of sheath current by wavelet transform, are used to construct cable state recognition matrix. The mutational signal is detected by the cumulative sum (CU-SUM) method and intercepted by a set time window. Secondly, a 7-layer deep CNN is constructed according to the features of recognition matrix. Then the CNN model is trained by the adaptive moment estimation (Adam) method to get the recognition model of cable state. Finally, the proposed method is used to recognize cable early state by large number of samples which are obtained from the simulation of four cable states with PSCAD software. Compared with other methods, the results of simulation demonstrate that the proposed method has a high recognition accuracy.

中文翻译：

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一种基于护套电流的10kV单芯电缆早期CNN识别方法

摘要 传统的电缆早期状态识别分析主要基于电气特性的一个或几个阈值，但阈值的计算往往受到测量精度和外界干扰的影响，不可避免地降低了识别精度。人工智能的发展为解决这一问题提供了新的途径。本文提出了一种基于护套电流的10 kV单芯电缆早期状态的深度卷积神经网络(CNN)识别方法。首先，利用小波变换从鞘流突变信息中提取的波形和能量特征，构建电缆状态识别矩阵。突变信号通过累积和（CU-SUM）方法检测并通过设定的时间窗口截获。第二，根据识别矩阵的特征构建了一个7层深度的CNN。然后通过自适应矩估计（Adam）方法对CNN模型进行训练，得到线缆状态的识别模型。最后，利用PSCAD软件对四种缆索状态进行模拟得到的大量样本，将所提出的方法用于缆索早期状态的识别。与其他方法相比，仿真结果表明该方法具有较高的识别准确率。所提出的方法用于通过PSCAD软件对四种电缆状态进行模拟而获得的大量样本来识别电缆早期状态。与其他方法相比，仿真结果表明该方法具有较高的识别准确率。所提出的方法用于通过PSCAD软件对四种电缆状态进行模拟而获得的大量样本来识别电缆早期状态。与其他方法相比，仿真结果表明该方法具有较高的识别准确率。