针对传统的选极方法存在着抗噪、抗高阻能力弱以及阈值整定复杂等问题，提出一种基于Res-BiLSTM网络的柔性直流配电线路故障选极方法。首先，对原始故障信号进行完全自适应噪声模态分解，再采用相关系数和香农熵进行重构得到重构信号；其次，搭建Res-BiLSTM网络模型进行选极，为提高网络精度与收敛速度，在分裂注意力网络中引入通道注意力模块，并使用卷积双向长短期记忆网络与改进分裂注意力网络同时提取重构信号特征，使用注意力特征融合模块融合提取到的特征，并对融合特征进行分类；最后，利用PSCAD/EMTDC搭建模型并验证所提方法。仿真结果表明所提选极方法准确性高，抗干扰能力强，不受故障距离影响。

Aiming at the problems of anti-noise, anti-high resistance and complex threshold setting of traditional pole selection methods, a fault selection method of flexible DC distribution line based on Res-BiLSTM network was proposed. Firstly, the original fault signal was subjected to complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then the reconstructed signal was obtained by using the correlation coefficient and Shannon entropy for reconstruction. Secondly, the Res-BiLSTM network model was constructed for the pole selection. In order to improve the network accuracy and the convergence speed, the channel attention module was introduced into the split-attention network. The reconstructed signal features were extracted using the convolutional bidirectional long short-term memory and the improved split-attention network at the same time. The extracted features were fused using the attention feature fusion module, and the fused features are classified. Finally, PSCAD/EMTDC was employed to construct the model and to verify the proposed methodology. The simulation results show that the proposed pole selection method is highly accurate, anti-interference, and independent of fault distance.