Application of Digital Twins to Overhead Line Conductors

Transmission line monitoring is undergoing a significant transformation driven by the increasing demand for enhanced grid utilization, Dynamic Line Rating (DLR), and cost-effective monitoring solutions. However, the large-scale deployment of physical monitoring devices remains constrained by installation and maintenance time and costs. This paper presents the development and validation of an artificial neural network (ANN)-based Digital Twin (DT) framework for conductor temperature monitoring through virtual sensing. The investigated approach is part of a multi-layer Digital Twin architecture that integrates data preparation, virtual sensing, and DLR applications into a unified monitoring concept. The research was conducted within the Horizon Europe TwinEU project using datasets collected from three power system operators operating overhead lines at voltage levels between 132 kV and 400 kV. The developed ANN models applied weather observations and SCADA-derived loading data as inputs, while conductor temperature measurements obtained from physical sensors served as training and validation targets. To enhance robustness and generalization, different data-splitting methodologies, normalization techniques, and site-specific hyperparameter optimization strategies were investigated using a Bayesian optimization framework. The results demonstrate that ANN-based virtual sensors can reproduce conductor temperature measurements with high accuracy, achieving RMSE values of 0.791–1.087 °C, when sufficient high-quality training data are available. The study also highlights the importance of local weather measurements and seasonally representative datasets for reliable model performance. The findings confirm that DT technologies can partially substitute physical monitoring assets without significant loss of accuracy, reducing monitoring complexity and operational costs. The proposed approach offers a practical pathway toward scalable transmission line monitoring and provides a foundation for future applications in DLR, ampacity calculation, market-oriented grid operation, and asset management.