Abstract
This article presents DynaLiRD, a comprehensive dataset for dynamic line rating (DLR) of the Trang-Thap Cham 220 kV overhead transmission line. The DLR values are computed using the IEEE 738-2012 standard based on historical meteorological data such as ambient temperature, wind speed and direction, and global horizontal irradiance as well as detailed line parameters including conductor type, diameter, length, and elevation. To enhance the dataset's applicability in cybersecurity and machine learning research, adversarially perturbed data is included using the fast gradient sign method (FGSM) and basic iterative method (BIM) under varying perturbation intensities. This dataset is essential for DLR estimation, dynamic thermal rating (DTR) forecasting, renewable energy integration into the grid, machine learning (ML) applications, infrastructure planning, energy policy development, and cybersecurity vulnerability investigation. Its structured format and inclusion of both clean and adversarial data make it valuable for evaluating the resilience of data-driven energy systems.