Lightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed scheme, the voltage and current characteristic matrix is constructed as the input matrix of BR-CNN model and the output categories include positive pole-to-ground (PTG) fault and lightning disturbance. Voltage and current branches are constructed to extract high-level local features of input data layer by layer, and main branch is designed to realize the comprehensive utilization of voltage and current information. Through autonomous learning of the model, the nonlinear mapping relationship between input and output is constructed. The method only uses the single-terminal quantities, and can be used as an auxiliary criterion to improve the reliability of the primary protection. The test results verify the effectiveness of the method, and the recognition accuracy is better than the traditional classification models.

中文翻译：

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基于分支结构卷积神经网络的柔性直流电网故障辅助识别策略

雷电干扰可能会被灵活高压直流 (HVDC) 电网中的初级保护误判为直流故障。针对这一问题，本文提出了一种基于具有分支结构的卷积神经网络（BR-CNN）的辅助故障识别策略。在所提出的方案中，电压电流特征矩阵被构建为BR-CNN模型的输入矩阵，输出类别包括正极对地（PTG）故障和雷电干扰。构建电压电流分支，逐层提取输入数据的高层局部特征，设计主分支，实现电压电流信息的综合利用。通过模型的自主学习，构建输入输出之间的非线性映射关系。该方法仅使用单端量，可作为提高一次保护可靠性的辅助判据。测试结果验证了该方法的有效性，识别准确率优于传统分类模型。