Research on wind deflection risk early warning method of transmission line driven by digital twin and data fusion

To improve the complex dynamic transmission lines monsoon weather environment risk early warning and refinement management capability, this paper proposes a digital twin and multi-source data fusion-driven transmission lines monsoon risk early warning method. First, a multi-source heterogeneous data system is constructed, which integrates meteorological, geographic, line ontology, and real-time monitoring data. Based on high-precision three-dimensional modelling and physical attribute binding technology, the digital twin of transmission lines is established and the bidirectional dynamic mapping between physical entity and virtual model at the geometry, attribute and state levels is realized. Beyond the one-way mapping from physical entity to virtual model, a bidirectional dynamic feedback mechanism is designed: real-time monitoring data continuously update the twin state, while the twin's simulation results (e.g., predicted wind-induced responses) are fed back to guide online sensor calibration and inspection strategies, thereby closing the loop between physical and digital spaces at geometry, attribute, and state levels. Next, the temporal and spatial heterogeneity of multi-source data, which are designed based on the deep learning framework of multimodal data fusion model, realize the weather forecast, the geographical environment, and collaborative analysis and dynamic structural response line deduction. Further, by integrating the dynamic mechanical response of the line with its electrical insulation characteristics, the critical state under monsoon conditions and the corresponding dynamic safety thresholds are defined. A real-time probabilistic risk assessment model is then established, enabling a paradigm shift from static threshold-based early warning to dynamic, evolution-based risk early warning. Finally, selecting typical typhoon influence area on the southeastern coast of China's 220 kv transmission line, the presented method is introduced in detail, from the front-end data integration, twin model driven, fusion algorithm operation to the early warning information to generate the whole process of application, and through comparing analysis of early warning effectiveness, more groups of data form. The results show that the warning accuracy of the proposed method is 92.3% and the average effective warning advance time is 98 minutes. Compared with the traditional warning method based on wind speed at meteorological stations, the spatial accuracy and time resolution of the proposed method are significantly improved, which provides more accurate and reliable decision support for the disaster prevention and mitigation and intelligent operation and maintenance of the power grid under extreme weather.