Load Forecasting and Transformer Capacity Optimization for Newly-built Traction Substation

In order to predict the load of a newly-built traction substation and optimize the traction transformer capacity, Gaussian mixture model was employed for clustering the measured data of traction loads, and then the neural network is introduced to match and assign the new traction load. According to the results of clustering and matching, the new load process for new electrified railway is evaluated by the probability density and Monte Carlo method. Then based on the theory of heat transfer and the calculation of aging rates, the difference equation models of the temperature rise and loss of life is proposed to optimize the capacity of new transformer. After analyzing large amount of measured data from traction substations, the pseudo-F value of the clustered data is 12.81, and rises to 12.90 when new load is matched and assigned, indicating that the clustering and assigning methods are effective. The capacity utilization ratio in case study rises from 60% to 96% by modeling the traction transformers. Even with safety margin, the capacity should be expanded and the capacity utilization ratio is also 75%, which achieves the goal of capacity optimization and makes the best of the temperature rise and loss of life.