现有的故障诊断手段面对复杂电网时，难以精确提取故障特征，急需适应性强、识别率高的故障诊断方法。鉴于此,提出一种基于压缩感知与并联卷积神经网络(CNN)和长短期记忆网络(LSTM)结合的电网故障诊断方法。搭建永富直流输电系统模型采集原始故障数据，原始故障数据应用压缩感知原理进行压缩采样，获得压缩域故障信号，以提高网络的计算效率。然后搭建了麻雀搜索算法（SSA）优化的并联CNN-LSTM深度学习模型。通过SSA确定并联CNN-LSTM的网络结构及参数，利用并联CNN-LSTM深度学习模型直接在故障的压缩域挖掘故障波形特征和时序特征，并对故障进行识别。仿真结果表明该模型相较于传统方法具有更高的故障诊断精度。

The traditional fault diagnosis methods have difficulty in accurately extracting fault characteristics in the case of complex power grids, and the fault diagnosis methods with high adaptability and recognition rates are urgently needed. A power grid fault diagnosis method based on the combination of compressed sensing, parallel convolutional neural network (CNN) and long short-term memory (LSTM) is proposed. A model of Yongfu DC transmission system is built to collect raw fault data, and the raw fault data are compressed and sampled by applying the principle of compressed sensing to obtain compressed domain fault signals to improve the computational efficiency of the network. Then, a parallel CNN-LSTM deep learning model with sparrow search algorithm (SSA) is built. The network structure and parameters of the parallel CNN-LSTM are determined by the SSA. The parallel CNN-LSTM deep learning model is used to mine the fault waveform and timing features directly in the compressed domain of the fault and identify the fault. The simulation results verify that the model has higher fault diagnosis accuracy compared with the traditional methods.