电气传动2026，Vol.56Issue(8)：79-87,9.DOI:10.19457/j.1001-2095.dqcd26611
基于ASFF-DC1D-CNN的变电站典型复合故障自动化辨识
Automatic Identification of Typical Compound Faults in Substation Based on ASFF-DC1D-CNN
摘要
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.关键词
变电站/复合故障识别/模拟量-开关量融合特征/卷积神经网络Key words
substation/multi-fault identification/analog-switch fusion features(ASFF)/convolutional neural network(CNN)分类
信息技术与安全科学引用本文复制引用
王存超,戴威,徐滔,张强,黄建宏,陈金刚..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(8):79-87,9.基金项目
国网江苏省电力有限公司科技项目(J2023160) （J2023160）
