基于CABFAM-Transformer的输电线路在线测距实测行波预分类方法  

Pre-Identification Method of Measured Traveling Wave Data for Online Fault Location of Transmission Line Based on CABFAM-Transformer

作　　者：唐玉涛 束洪春[1,2] 刘皓铭 苏萱 韩一鸣 代月 Tang Yutao;Shu Hongchun;Liu Haoming;Su Xuan;Han Yiming;Dai Yue(State Key Laboratory of Collaborative Innovation Center for Smart Grid Fault Detection,Protection and Control Jointly,Kunming University of Science and Technology,Kunming,650051,China;School of Electric Power Engineering,Kunming University of Science and Technology,Kunming,650051,China)

机构地区：[1]省部共建智能电网故障检测与保护控制协同创新中心(昆明理工大学),昆明650501 [2]昆明理工大学电力工程学院,昆明650501

出　　处：《电工技术学报》2025年第5期1455-1470,共16页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金重点资助项目(52337005)。

摘　　要：行波采集装置是电力系统保护与测距的重要设备,广泛应用于110 kV及以上输电线路,且正向配电网延拓。由于启动灵敏,非故障信息的采集给故障辨识与行波测距带来了挑战。该文提出了一种基于卷积注意力机制的特征聚合模块(CABFAM)与自适应Transformer模型的输电线路实测故障性质识别方法。首先,通过CBAM机制增强卷积层提取特征信息的表达与理解能力;然后,构建自适应编码层级调整机制的Transformer模型库,以获取多层次差异化特征信息;最后,利用云南电网110~220 kV输电线路的5076条实测数据及220 kV DL站H-P线的15924条伪实测数据进行训练与测试,针对16种典型行波数据进行分类。测试结果表明,该方法降低了模型参数量,提高了准确度,算法的多个关键指标均有不同幅度的提升,表现出优异的检测精度与识别效率。This paper presents an innovative approach to fault identification for transmission lines by integrating a convolutional attention-based feature aggregation module(CABFAM)with an adaptive Transformer model.Traditional traveling wave acquisition systems often suffer from frequent triggering,significant noise interference,and difficulty in detecting weak fault signals.To address these limitations,this study proposes a method that combines the strengths of convolutional feature extraction and Transformer-based multi-layer encoding.The objective is to improve the reliability,accuracy,and efficiency of fault identification,providing robust support for the safe operation of power systems.The methodology involves a hybrid architecture that leverages both convolutional modules and Transformer encoders.Specifically,the convolutional block attention module(CBAM)enhances feature extraction by refining channel-wise and spatial features through an attention mechanism.These refined features are then processed by an adaptive Transformer-based encoder,which extracts multi-layered characteristics from complex fault data,ensuring comprehensive analysis and classification.A combination of real-world and simulated data was used to develop and validate the model.The dataset includes 5076 measured waveforms from 110-220 kV transmission lines in the Yunnan power grid and 15924 simulated fault scenarios.Key innovations include the use of SiLU(Sigmoid Linear Unit)activation functions to mitigate gradient vanishing problems,improving the stability of the backpropagation process.The model parameters were optimized through iterative training to balance computational efficiency and accuracy,ensuring scalability for large-scale applications.The proposed model demonstrates superior performance in fault detection and classification compared to traditional methods.Key metrics from the experimental evaluation include an mAUC of 0.982,an accuracy of 0.983,a precision of 0.981,and an F1-score of 0.974,indicating the robustness of the approach.Ablat

关 键 词：行波采集装置 基于卷积注意力机制的特征聚合模块 CABFAM 自适应Transformer 实测数据故障辨识 

分 类 号：TM773[电气工程—电力系统及自动化]