Dynamic line rating forecasting algorithm for a secure power system network

Abstract

This research aimed to enhance the capacity of transmission lines by developing an algorithm to predict Dynamic Line Rating (DLR) to avert the curtailment of renewable energy sources. This will help meet electricity demand, prevent power outages, reduce costs, and protect equipment and personnel. The proposed algorithm was trained by analyzing the correlation between sequential hourly DLR calculations derived from historical weather data and line physical parameters. The proposed algorithm used k-means clustering and Monte Carlo simulation to predict hourly DLR, considering the temporal correlation of historical DLR values for each month. The model's accuracy was verified through statistical tests and was compared to other forecasting methods such as ensemble forecasting, quantile regression, and recurrent neural networks. It was found that the proposed model demonstrated performance that is comparable or superior to existing methods, as seen in forecast skill ranging from 91 to 98%, a Continuous Ranked Probability Score (CRPS) between 0.02 and 0.05, and probabilistic ratings that are 48–70% higher than traditional Static Line Rating (SLR).