Under the condition of a strong electric field, partial discharge often occurs when insulated wire is damaged. The recognition of partial discharge is an effective method for the fast and accurate detection of high voltage insulated wire faults. This paper proposes a PD recognition algorithm based on a convolutional neural network and long short-term memory (LSTM). In addition, attention mechanisms are introduced to give separate weights to LSTM hidden states through a mapping, weighting, and learning parameter matrix. This is done to reduce the loss of historical information and to strengthen the influence of important information. The complex relationship between the voltage signal change and the grid operation state response has been established. The proposed method is verified by the ENET data set published by VSB University. The recognition accuracy is 95.16% for no-PD and 94.44% for PD. Results from the proposed algorithm show that this method has a higher detection accuracy.

在强电场条件下，绝缘电线损坏时，经常会发生局部放电。局部放电的识别是一种快速，准确地检测高压绝缘电线故障的有效方法。本文提出了一种基于卷积神经网络和长短期记忆（LSTM）的PD识别算法。另外，引入了注意机制，以通过映射，加权和学习参数矩阵为LSTM隐藏状态赋予单独的权重。这样做是为了减少历史信息的丢失并增强重要信息的影响力。已经建立了电压信号变化与电网运行状态响应之间的复杂关系。该方法已由VSB大学发布的ENET数据集进行了验证。对于非PD，识别精度为95.16％，对于PD，则为94.44％。所提算法的结果表明，该方法具有较高的检测精度。