Pre-Identification Method of Measured Traveling Wave Data for Online Fault Location of Transmission Line Based on CABFAM-Transformer
Tang Yutao1, Shu Hongchun1,2, Liu Haoming2, Su Xuan2, Han Yiming2, Dai Yue1
1. 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; 2. School of Electric Power Engineering Kunming University of Science and Technology Kunming 650051 China
Abstract：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 5 076 measured waveforms from 110-220 kV transmission lines in the Yunnan power grid and 15 924 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. Ablation studies further revealed that integrating the CBAM module improved model performance by more than 3 percentage points, while the adaptive Transformer structure contributed an additional 4.5 percentage points increase in key performance indicators. Comparative analysis with existing fault identification techniques highlights the advantages of the proposed model. It not only improves classification accuracy but also maintains high efficiency under noisy conditions and weak fault scenarios. The model's ability to handle diverse fault types and maintain low computational overhead makes it suitable for real-time applications. Additionally, the CABFAM-Transformer framework efficiently processes high-dimensional traveling wave data without compromising on speed, supporting fast decision-making for power grid operators. The results of this study confirm that the CABFAM-Transformer-based fault identification method offers significant improvements over traditional approaches in terms of accuracy, robustness, and efficiency. By combining convolutional attention modules with adaptive Transformer encoders, the framework addresses the challenges posed by noisy data and weak fault signals, ensuring reliable detection across a wide range of scenarios. The use of SiLU activation functions further enhances the model's performance by stabilizing the training process and preventing gradient issues. This method provides a scalable solution for real-world applications in power grid fault management, with the potential to improve operational efficiency and reduce downtime. Future research will explore further optimization of the Transformer architecture to reduce computational costs and enhance real-time capabilities. The application of this model to larger-scale datasets and diverse grid environments will also be investigated, with the aim of developing a comprehensive fault management system for modern power networks.
唐玉涛, 束洪春, 刘皓铭, 苏萱, 韩一鸣, 代月. 基于CABFAM-Transformer的输电线路在线测距实测行波预分类方法[J]. 电工技术学报, 2025, 40(5): 1455-1470.
Tang Yutao, Shu Hongchun, Liu Haoming, Su Xuan, Han Yiming, Dai Yue. Pre-Identification Method of Measured Traveling Wave Data for Online Fault Location of Transmission Line Based on CABFAM-Transformer. Transactions of China Electrotechnical Society, 2025, 40(5): 1455-1470.
[1] 戴志辉, 牛宝仪, 李铁成, 等. 基于控保协同的三端混合直流输电系统线路保护方法[J]. 电工技术学报, 2025, 40(1): 108-121. Dai Zhihui, Niu Baoyi, Li Tiecheng, et al.Line protection method of three-terminal hybrid DC transmission system based on control and protection coordination[J]. Transactions of China Electro-technical Society, 2025, 40(1): 108-121. [2] 郑涛, 陈云飞, 马英, 等. 基于复合暂态能量的多端柔性直流电网自适应差动保护[J]. 电工技术学报, 2025, 40(5): 1440-1454. Zheng Tao, Chen Yunfei, Ma Ying, et al.Differential protection for multi terminal flexible DC power grid based on composite transient energy[J]. Transactions of China Electrotechnical Society, 2025, 40(5): 1440-1454. [3] 许水清, 许晓凡, 何怡刚, 等. 基于自适应滑模观测器的3L-NPC并网逆变器开关管和电流传感器故障诊断[J]. 电工技术学报, 2024, 39(13): 4066-4078. Xu Shuiqing, Xu Xiaofan, He Yigang, et al.A diagnosis method for power switch and current sensor faults in grid-connected three-level NPC inverters based on adaptive sliding mode observer[J]. Transactions of China Electrotechnical Society, 2024, 39(13): 4066-4078 [4] 邱大林. 110 kV T接输电线路单端行波测距方法的研究[D]. 昆明: 昆明理工大学, 2019. Qiu Dalin.Research on single-ended traveling wave distance measurement method in 110 kV T transmission lines[D]. Kunming: Kunming University of Science and Technology, 2019. [5] 束洪春, 田鑫萃, 吕蕾. 基于多测度的闪络故障行波与干扰杂波辨识[J]. 电力系统自动化, 2017, 41(19): 128-134. Shu Hongchun, Tian Xincui, Lü Lei.Multi-measure based identification of flashover fault traveling wave and interference noise[J]. Automation of Electric Power Systems, 2017, 41(19): 128-134. [6] 卢晓强, 李刚, 周万竣, 等. 基于最小二乘双支持向量机的配电网短路故障辨识方法研究[J]. 武汉大学学报(工学版), 2022, 55(4): 401-408. Lu Xiaoqiang, Li Gang, Zhou Wanjun, et al.Research on identification method of short-circuit fault in distribution network based on least squares twin support vector machine[J]. Engineering Journal of Wuhan University, 2022, 55(4): 401-408. [7] 童晓阳, 罗忠运. 基于模糊支持向量机的输电线路故障模糊分类方法及其降维显示[J]. 高电压技术, 2015, 41(7): 2276-2282. Tong Xiaoyang, Luo Zhongyun.Transmission line faults classification method based on fuzzy support vector machine and reducing-dimension display[J]. High Voltage Engineering, 2015, 41(7): 2276-2282. [8] Shi Shenxing, Zhu Beier, Mirsaeidi S, et al.Fault classification for transmission lines based on group sparse representation[J]. IEEE Transactions on Smart Grid, 2019, 10(4): 4673-4682. [9] Liao Wenlong, Bak-Jensen B, Radhakrishna Pillai J, et al.A review of graph neural networks and their applications in power systems[J]. Journal of Modern Power Systems and Clean Energy, 2022, 10(2): 345-360. [10] 何志鹏, 李伟林, 邓云坤, 等. 低压交流串联故障电弧辨识方法[J]. 电工技术学报, 2023, 38(10): 2806-2817. He Zhipeng, Li Weilin, Deng Yunkun, et al.The detection of series AC arc fault in low-voltage distribution system[J]. Transactions of China Electro-technical Society, 2023, 38(10): 2806-2817. [11] 张广斌, 陈柏宇, 束洪春, 等. 基于时域波形特征认知的输电线路近端故障辨识与定位[J]. 电力系统自动化, 2024, 48(5): 146-156. Zhang Guangbin, Chen Boyu, Shu Hongchun, et al.Identification and location of near-end faults in transmission lines based on time-domain waveform characteristic recognition[J]. Automation of Electric Power Systems, 2024, 48(5): 146-156. [12] Chen Kunjin, Hu Jun, He Jinliang.Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder[C]//2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 2017: 1. [13] Szegedy C, Liu Wei, Jia Yangqing, et al.Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015: 1-9. [14] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770-778. [15] Oquab M, Bottou L, Laptev I, et al.Learning and transferring mid-level image representations using convolutional neural networks[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 1717-1724. [16] Tan Mingxing, Le Q V.EfficientNet: rethinking model scaling for convolutional neural networks [EB/OL]. 2019: 1905. 11946. https://arxiv.org/abs/1905.11946v5 [17] Goodfellow I, Bengio Y, Courville A.Deep Learning Adaptive Computation and machine learning series[M]. Cambridge, MA: The MIT Press, 2016. [18] Krizhevsky A, Sutskever I, Hinton G E.ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. [19] Cao Di, Hu Weihao, Zhao Junbo, et al.Reinforcement learning and its applications in modern power and energy systems: a review[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(6): 1029-1042. [20] Chen Bairen, Wu Q H, Li Mengshi.et al.Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks[J]. Prot Control Modern Power Systems, 2023, 8: 16. [21] 袁嘉玮, 焦在滨. 基于零序信号全波形识别的小电流接地系统故障选线方法[J]. 电网技术, 2024, 48(2): 839-853. Yuan Jiawei, Jiao Zaibin.Faulty feeder detection in small current grounding system based on complete waveform recognition of zero-sequence signals[J]. Power System Technology, 2024, 48(2): 839-853. [22] 徐舒玮, 邱才明, 张东霞, 等. 基于深度学习的输电线路故障类型辨识[J]. 中国电机工程学报, 2019, 39(1): 65-74, 321. Xu Shuwei, Qiu Caiming, Zhang Dongxia, et al.A deep learning approach for fault type identification of transmission line[J]. Proceedings of the CSEE, 2019, 39(1): 65-74, 321. [23] Vaswani A, Shazeer N, Parmar N, et al.Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [24] Dosovitskiy A, Beyer L, Kolesnikov A, et al.An image is worth 16×16 words: transformers for image recognition at scale[J]. ArXiv e-Prints, 2020: arXiv: 2010. 11929. [25] Zhao Xudong, Zhang Mengmeng, Tao Ran, et al.Fractional Fourier image transformer for multimodal remote sensing data classification[J]. IEEE Transa-ctions on Neural Networks and Learning Systems, 2024, 35(2): 2314-2326. [26] Morel M, Achard C, Kulpa R, et al.Time-series averaging using constrained dynamic time warping with tolerance[J]. Pattern Recognition, 2018, 74: 77-89. [27] 陈柏元. 电力输电线路故障分类方法及其应用研究[D]. 重庆: 重庆理工大学, 2018. Chen Boyuan.Research on fault classification method and its application of power transmission line [D]. Chongqing: Chongqing University of Technology, 2018. [28] Rakov V A, Borghetti A, Bouquegneau C, et al.CIGRE technical brochure on lightning parameters for engineering applications[C]//2013 International Sym-posium on Lightning Protection (XII SIPDA), Belo Horizonte, Brazil, 2013: 373-377.
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