An Early Warning Method for Abnormal Fluttering Behavior in Transmission Lines Based on Deep Binocular Vision
DOI:
https://doi.org/10.4108/ew.12827Keywords:
Deep Binocular Vision, Power Transmission Lines, Morphological Edges, Background Suppression, Flutter Anomaly Behavior, Early Warning MethodAbstract
Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a threat to the safety of the power grid. The existing monitoring methods suffer from inaccurate 3D spatial data acquisition, leading to errors in edge detection, difficulty in suppressing background interference, insufficient feature extraction, and reduced accuracy in detecting abnormal oscillations. Therefore, this article proposes a warning method based on deep binocular vision, which captures three-dimensional data with a stereo camera and converts it into two-dimensional images. Mathematical morphological operators are used to determine the edge of the transmission line, solving the problem of inaccurate edge detection. The morphological edge results are input into the ResNet LSTM network to suppress background noise and extract features. Finally, compare the detection results with the actual values to achieve multi-level alarms. The experiment shows that this method is efficient and accurate, with a detection efficiency of 0.958-0.968. Improved the reliability of early warning.
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[1] Jegathesan, V., Jebaseeli, T. J., & David, D. J. Identification of Faults in Invisible Underground Broken Transmission Lines. IETE journal of research. 2023; 69(9):6505-6511.
[2] Sankuri, R. S., Sristy, N. B., & Karri, S. P. K. Syflo: augmenting yolo for real-time health monitoring of electric assets in power transmission lines. Journal of Real-Time Image Processing. 2025; 22(1):1.1-1.15.
[3] Ayambire, P. N., Huang, Q., Awopone, A. K., Jian, L., Anane, P. O. K., & Bamisile, O., et al. A non-contact current traveling wave fault location method for multi-terminal overhead power lines based on adaptive mathematical morphology. Electrical engineering. 2025; 107(1):191-204.
[4] Gonzalez-Fernandez, D., Rezgui, D., De Risi, R., Macdonald, J. H. G., Matsumiya, H., & Titurus, B. Identification and analysis of experiment-driven model for galloping in multi-conductor transmission lines. Engineering structures. 2025; 326:119344.
[5] Fayazi, M., Saffarian, A., Joorabian, M., & Monadi, M. An AI-based fault detection and classification method for hybrid parallel HVAC/HVDC overhead transmission lines. Electric Power Systems Research. 2025; 238:11083.
[6] Shakiba, F. M., Azizi, S. M., Zhou, M., & Abusorrah, A. Application of machine learning methods in fault detection and classification of power transmission lines: a survey. Artificial Intelligence Review: An International Science and Engineering Journal. 2023; 56(7):5799-5836.
[7] Fekri, M., Nikoukar, J., & Gharehpetian, G. B. Vulnerability risk assessment of electrical energy transmission systems with the approach of identifying the initial events of cascading failures. Electric Power Systems Research. 2023; 220:109271.
[8] Garcia-Salguero, M., Dima, E., Mateus, A., & Gonzalez-Jimenez, J. Fast Certifiable Algorithm for the Absolute Pose Estimation of a Camera. SIAM Journal on Imaging Sciences. 2024; 17(3):1415-1432.
[9] Kahnamouei, A. S., & Lotfifard, S. Distribution Systems Fault Location Identification Using Mixed Datasets. IEEE Transactions on Power Delivery. 2025; 40(2):951-964.
[10] Amiri, M. H., Pourgholi, M., Hashjin, N. M., & Ardakani, M. K. Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system. Soft computing. 2024; 28(20):12141-12174.
[11] Raju, G. V., & Srikanth, N. V. A novel protection scheme for transmission lines connected to solar photovoltaic and wind turbine farms using fuzzy logic systems and bagged ensemble learning. Electrical engineering. 2024; 106(6):7509-7529.
[12] Mehdi, A., Hassan, S. J. U., & Kim, C. H. Squaring and lowpass filtering data-driven technique for AC faults in AC/ DC lines. Electric Power Systems Research. 2023; 223:1.1-1.8.
[13] Sejai, M., Mansouri, A., Bennani, S. D., & Ruichek, Y. Real Time Implementation of Inter-Car Distance Based on an Intelligent Stereovision System for Autonomous Vehicles. International journal of intelligent transportation systems research. 2025; 23(1):525-533.
[14] Fu Q., Kong J. M., Ji Y. F., & Ren F. H. Accurate Stereo Matching for Processing Binocular Vision Image Edge Region. Computer Simulation. 2023; 40(12):226-231.
[15] Ahmad, S., Naeem, F., & Tariq, M. Intelligent Reflective Surfaces Assisted Vehicular Networks: A Computer Vision-Based Framework. IEEE transactions on intelligent transportation systems. 2025; 26(4):4481-4490.
[16] Jeon, Y., Tran, D. Q., Kulinan, A. S., Kim, T., Park, M., & Park, S. Vision-based motion prediction for construction workers safety in real-time multi-camera system. Advanced engineering informatics. 2024; 62:102898.
[17] Bai W.W., Li, J.J., Chen, F. Simulation of Crop Picking Based on Binocular Image Depth Learning. Computer Simulation. 2024; 41(2):187-191.
[18] Gupta, M., Wadhvani, R., & Rasool, A. A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-based systems. 2023; 259:1-22.
[19] Mirshekali, H., Keshavarz, A., Dashti, R., Hafezi, S., & Shaker, H. Deep learning-based fault location framework in power distribution grids employing convolutional neural network based on capsule network. Electric Power Systems Research. 2023; 223:109529.
[20] Biswas, S., Nayak, P. K., Panigrahi, B. K., & Pradhan, G. An intelligent fault detection and classification technique based on variational mode decomposition-CNN for transmission lines installed with UPFC and wind farm. Electric Power Systems Research. 2023; 223:109526.
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Copyright (c) 2026 Zhimeng Zhang, Jie Liu, Cuiying Sun, Xiaoyu Yi, Yixin Wang
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