Predictive Dynamic Thermal Rating Models for the Transmission Line
Authors: Abhinav Bhattarai, Tanus Bikram Malla, Apsara Adhikari, Bishal Silwal, and Brijesh Adhikary
Abstract—Dynamic thermal rating (DTR) technology offers important benefits over the conventional static thermal rating (STR) methodology by safely evaluating the thermal constraints of power components based on climatic circumstances and enabling the running of existing grids with greater flexibility since it reveals latent power capabilities of the transmission lines. The study utilizes IEEE standard 738 to calculate the DTR of the transmission. Furthermore, the predictive DTR model is developed using Gaussian process regression, support vector machine, regression tree, and neural network algorithms. The performance of these predictive models is compared with DTR calculated using IEEE standard 738 where IEEE standard 738 is used as a reference. On implementing the predictive models regression tree is found to be computationally fast whereas, Gaussian process regression is more accurate.
Keywords—IEEE standard 738, DTR, ML, DLR, Predictive DTR model
Full Paper: to be purchased for $20 (contact us at [email protected])
Published In: International Conference on Role of Energy for Sustainable Social Development (RESSD-2023)
Date of Conference: 14th-15th May 2023
Conference Location: Kathmandu, Nepal
Publisher: IEEE Power and Energy Society Nepal Chapter
Cite the paper as:
A. Bhattarai, T. B. Malla, A. Adhikari, B. Silwal, and B. Adhikary, “Predictive Dynamic Thermal Rating Models for the Transmission Line”, International Conference on Role of Energy for Sustainable Social Development, 14th-15th May 2023, Kathmandu, Nepal