Abstract：The multi-faults of substation are of great significance for the stability of power system. In order to

automatically identify the typical faults of substation，a dual-channel one-dimensional convolutional neural network

（DC1D-CNN）model based on analog-switch fusion features（ASFF）was proposed. The model was consists of

two independent feature processing modules and a shared decision module，which could utilize the features of

different types of data. Batch normalization layer（BN）and attention mechanism（AM）structure were added into

the traditional one-dimensional convolutional neural network（1D-CNN）model. The analog features of the

recording signal and the digital features of the switching signal could be efficiently fused by using dual-channel，

which could enhance the ability of feature extraction and learning of the model. A typical 220 kV intelligent

substation was modeled and simulated. The data obtained from the simulation were employed to train and test the

model. The results indicated that the model achieved a recognition accuracy of 98.33% for typical faults.

Compared with the traditional single-channel CNN，backpropagation neural network（BPNN），and recurrent neural network（RNN）algorithm，the proposed method has great efficiency and robustness. It performs better in the face of complex data.

        Key words：substation；multi-fault identification；analog-switch fusion features（ASFF）；convolutional

neural network（CNN）

     Format Citation:王存超，戴威，徐滔,等. 基于ASFF⁃DC1D⁃CNN的变电站典型复合故障 自动化辨识[J].电气传动,2026,56(08):79-87. WANG Cunchao，DAI Wei，XU Tao,et al. Automatic identification of typical compound faults in substation based on ASFF-DC1D-CNN [J].Electric Drive, 2026,56(08): 79-87